본 연구는 일본 가고시마현을 대상으로, 이 지역이 고구마 소주의 대 표적인 산지로 자리매김하기까지의 사회문화적 맥락을 검토하고, 지역특 산품을 생산하는 공간인 소주 공장의 가치 및 역할과 구체적인 활용 사 례를 분석하였다. 이를 위해 문헌 자료와 사회관계망서비스에 공유되는 온라인 자료들을 분석하고, 2024년 1월 가고시마현 소주 공장을 방문하 여 관계자들을 면담했다. 연구결과, 고구마 소주는 그 처리법과 제조기술 이 부족하여 초기에는 쌀 소주에 비해 맛과 향이 좋지 않았다. 군비 근 대화 과정에서 양산되고 지역특산품으로 만들어지는 과정에서 끊임없는 연구와 기술개발이 이루어지면서 ‘본격소주’라고 불릴 만큼 사회적으로 그 품질을 인정받게 되었다. 지역의 역사와 문화가 응축된 소주 공장은 산업유산이자 일시적 의례 공간으로 지역 활성화 관점에서 다각적으로 활용되고 있다. 지역의 소주 공장을 순회하는 ‘소주장 순례’는 가고시마 현이 소주 산지라는 통합적 이미지를 형성하지만, 각 공장의 역사와 입 지, 자본 규모 등에 따라 환대, 해설형태, 전시물, 지역민의 참여 등 활 용 정도에 있어 차이가 존재했다. 최근에는 전문가 집단이 협의체를 구 성하고 소주 공장과 지역민이 참여하는 형태의 다각적인 프로그램도 만 들어지고 있다. 이러한 연구결과는 한국 증류식 소주 산업 관계자들이 거버넌스를 활성화하고, 양조장 활용 방안을 모색하는데 시사점을 제시 할 것이다.

작물을 재배하는 데 필요한 여러 가지 환경 조건 중 광은 개 화와 밀접한 연관이 있다. 본 연구는 식용화, 매리골드 화아분 화에 영향을 주는 최적의 광주기를 구명하여 완전제어형 식물 공장에서 효율적으로 재배하기 위해 진행되었다. 실험에 사 용된 광주기는 4, 8, 12, 16시간, 총 4가지로 설정하였다. 매리 골드 ‘듀란고 레드’ 종자를 우레탄 스펀지에 파종한 직후부터 광주기를 처리하였다. 화아분화는 꽃봉오리가 약 2mm 이상 일 때 화아분화가 되었다고 정의하였고, 2－3일 간격으로 조 사하였다. 생육 조사는 지상부의 생체중, 건물중, 초장, 엽면 적을 조사하였다. 최적의 광주기는 식물체의 50%가 화아분 화 된 날을 기준으로 정의하였다. 4시간 처리구에서는 식물체 가 제대로 자라지 못하며 화아도 형성되지 않았다. 8시간 이상 의 처리구에서부터 식물체가 정상적으로 생장하고 화아분화 가 이루어졌지만, 8시간 처리구는 12시간 이상의 처리구에 비 해 화아분화가 더디게 일어났다. 반면에 12시간 처리구와 16 시간 처리구는 서로 유의하지 않은 결과를 보였다. 모든 생육 조사 항목에서 16시간 광주기 처리구가 가장 높은 값을 나타 냈으나 지상부의 건물중과 엽면적을 제외한 나머지 항목에서 12시간 처리구와 유의하지 않았다. 실험 결과에 따르면, 8시간 광주기에서도 화아분화가 일어났지만, 화아형성까지의 시 간이 12시간 이상의 광주기일 때보다 더 많이 소요되었으며, 식물체의 생육 또한 12시간 이상의 광주기를 조사받은 식물 체보다 낮게 나타났다. 본 실험에서 에너지 소비량을 고려한 최적의 매리골드 ‘듀란고 레드’의 광주기는 12시간으로 판단 된다.

This study analyzed and identified various causes of caustic alarms of 163 fire detectors that occurred from January 2019 to December 2021 at domestic semiconductor manufacturing plants equipped with about 30,000 fire detectors, and proposed a new non-fire prevention cause investigation plan by applying the NFPA 921 scientific methodology. The results of the study are as follows. First, in terms of necessary recognition and problem definition, an analog detector and an integrated monitoring system were proposed to quickly determine the location and installation space information of the fire detector. Second, in order to prevent speculative causes and errors in various analyses in terms of data analysis and hypothesis establishment, non-fire reports were classified into five by factor and defined, and the causes of occurrence by factor were classified and proposed. Finally, in terms of hypothesis verification and final hypothesis selection, a non-fire prevention improvement termination process and a final hypothesis verification sheet were proposed to prevent the cause from causing re-error.

Recently, a closed-type plant factory has been receiving attention as a advanced agricultural method. It has diverse advantages such as climate-independence, high productivity and stable year-round production. However, high energy cost caused by environmental control system is considered as a challenges of a closed-type plant factory. In order to reduce the energy cost, investigation about energy load which is directly connected to energy consumption needs to be conducted. In this study, energy load changes of a plant factory have been analytically analyzed according to the environmental changes. The target plant factory was a lettuce growing container farm. Firstly, the impact of photoperiod, set temperature and relative humidity change were examined. Under the climate condition of Daejeon in South Korea, increase of photoperiod and set temperature rose a yearly energy demand of a container farm. However, increase of set relative humidity decreased a yearly energy demand. Secondly, the climate environment effect was compared by investigating the energy demand under 9 different climate conditions. As a result, the difference between maximum and minimum value of the yearly energy demand showed 21.7%. Lastly, sensitivity analysis of each parameter (photoperiod, set temperature and relative humidity) has been suggested under 3 different climate conditions. The ratio of heating and cooling demand was varied depending on the climate, so the effect of each parameter became different.

This study was aimed to determine the effects of grow media on the mineral contents of the leaves and growth characteristics of strawberry grown under aquaponics system in a plant factory. For aquaculture, 12 fish (Cyprinus carpio) (total weight, 2.0 kg) were raised in an aquaponics tank (W 0.7 m × L 1.5 m × H 0.45 m, 472.5 L) filled with 367.5 L of water at a density of 5.44 kg·m-3 and total 34 of strawberry seedlings were transplanted in the pots filed with 200 g of orchid stone, hydroball or polyurethane sponge in the growing bed (W 0.7 m × L 1.5 m × H 0.22 m) laid out with holly acrylic sheet (140×60 mm, Ø80) on the top of the system. The pH and EC of the aquaponic solution was ranged from 7.6 to 4.9 and 0.24－0.91 dS·m-1, respectively. The concentration of NO3-N was about 28% lower than that of the hydroponic standard solution, and K, Fe and B were 10, 27 and 3.8 times lower, respectively; however, the mineral contents of strawberry leaves were in the appropriate ranges with lower contents in the leaves grown with sponge media. The organic content (OM), nitrogen (N), phosphorus (P), and potassium (K) of the sludge were 61.5, 5.72, 8.92, and 0.24%, respectively. The leaf area, leaf number, and dry and fresh weights of shoot at 81 DAT were significantly higher in the hydroball, and the average number of fruits per plant was significantly higher in both the orchid stone and hydroball. There was no significant difference in the fresh and dry weights of fruits. Integrated all the results suggest that the orchid stone and hydroball media are more effective to utilize nutrients in solid particles of aquaponic solution, compared to the polyurethane sponge.

Growth modeling in plant factories can not only control stable production and yield, but also control environmental conditions by considering the relationship between environmental factors and plant growth rate. In this study, using the expolinear function, we modeled perilla [Perilla frutescens (L.) Britt.] cultivated in a plant factory. Perilla growth was investigated 12 times until flower bud differentiation occurred after planting under light intensity, photoperiod, and the ratio of mixed light conditions of 130 μmol·m-2·s-1, 12/12 h, red:green:blue (7:1:2), respectively. Additionally, modeling was performed to predict dry and fresh weights using the expolinear function. Fresh and dry weights were strongly positively correlated (r = 0.996). Except for dry weight, fresh weight showed a high positive correlation with leaf area, followed by plant height, number of leaves, number of nodes, leaf length, and leaf width. When the number of days after transplanting, leaf area, and plant height were used as independent variables for growth prediction, leaf area was found to be an appropriate independent variable for growth prediction. However, additional destructive or non-destructive methods for predicting growth should be considered. In this study, we created a growth model formula to predict perilla growth in plant factories.

Manufacturing process mining performs various data analyzes of performance on event logs that record production. That is, it analyzes the event log data accumulated in the information system and extracts useful information necessary for business execution. Process data analysis by process mining analyzes actual data extracted from manufacturing execution systems (MES) to enable accurate manufacturing process analysis. In order to continuously manage and improve manufacturing and manufacturing processes, there is a need to structure, monitor and analyze the processes, but there is a lack of suitable technology to use. The purpose of this research is to propose a manufacturing process analysis method using process mining and to establish a manufacturing process mining system by analyzing empirical data. In this research, the manufacturing process was analyzed by process mining technology using transaction data extracted from MES. A relationship model of the manufacturing process and equipment was derived, and various performance analyzes were performed on the derived process model from the viewpoint of work, equipment, and time. The results of this analysis are highly effective in shortening process lead times (bottleneck analysis, time analysis), improving productivity (throughput analysis), and reducing costs (equipment analysis).

본 연구는 같은 일적산광량(DLI) 조건에서 각각의 광강도 와 광주기 조합이 십자화과 식물 3종의 생육 및 글루코시놀레 이트(GSLs) 함량에 미치는 영향을 살펴보고자 수행되었다. 갓(Brassica juncea (L.) Czern.), 적겨자(Brassica juncea L.), 케일(Brassica oleracea L. var. acephala (DC.) Alef.) 종자를 암면 배지에 파종하여 3주간 육묘하였다. 육묘 후 DLI 를 10mol·m-2·d-1로 설정하여 10h-280, 14h-200, 18h-155, 22h-127μmol·m-2·s-1 처리 조합으로 3주간 처리하였다. 14h- 200μmol·m-2·s-1 처리한 결과, 적갓과 케일은 지상부 생체중/ 건물중, 엽수, 엽면적이 증가하였지만, 유의적 차이는 관찰되 지 않았다. 적갓의 GSLs 함량에서 14h-200μmol·m-2·s-1 처리 가 10h-280, 18h-155, 22h-127μmol·m-2·s-1 처리보다 각각 유의적으로 139.95, 135.87, 154.03% 증가하였으며, 케일의 경우 14h-200μmol·m-2·s-1 처리가 다른 처리구보다 각각 132.96, 122.06, 131.78% 유의하게 증가하였다. 적겨자는 18h-155μmol·m-2·s-1 처리에서 지상부 생체중/건물중, GSLs 함량이 증가하였으며, 14h-200μmol·m-2·s-1 처리에서 엽수가 10h-280, 18h-155, 22h-127μmol·m-2·s-1 처리보다 각각 15.62, 12.12, 32.14% 유의적으로 증가하였다. 유사한 십자 화과 작물이라도 종에 따라 DLI 반응이 다르기 때문에 최적의 DLI 조건 구명과 최소의 전력 소비량 및 최대 효율을 내기 위 한 광질 연구를 병행하여 더 세밀한 결과를 도출해야 할 것으 로 사료된다.

The purpose of this study is to suggest a plan to improve the level of acceptance of related technologies and the transition to smart factories of small and medium-sized manufacturing enterprises by using ‘technology readiness’ and ‘integrated technology acceptance model’. To this end, the research hypothesis was verified by collecting questionnaire data from 130 small and medium- sized manufacturing companies in Korea and conducting path analysis. First, optimism affects performance expectations, social influence, and facilitation conditions, innovation affects performance expectations, effort expectations, and social influence, discomfort affects performance expectations, social influence, and facilitation conditions, and anxiety affects effort expectations, social influence and facilitation conditions. has been proven to affect Finally, performance expectations, effort expectations, social influence, and facilitation conditions were verified to have a significant positive effect on the intention to accept technology.

식물공장에서 적합한 광을 선정하기 위해서는 양적인 측면 과 기능적 측면 뿐만 아니라 운영비를 고려하기 위하여 전기 에너지이용효율과 광이용효율을 동시에 고려해야 하는데 본 연구에서는 들깨를 위한 LED광원의 광량, 적색광과 청색광 의 혼합 비율과 광주기 조건별 생육 특성과 전기에너지이용효 율과 광이용효율을 함께 비교하였다. 광량 처리구는 60, 130, 230, 320μmol·m-2·s-1 조건으로, 광질 처리구는 적색광과 청 색광의 혼합 비율 8:2, 6:4, 4:6, 2:8 조건으로, 일장 처리구는 낮 기준 9, 12, 15, 18시간으로 처리하였다. 광량 실험에서는 광량이 높을수록 생육량이 늘어나는데 비해 소비전력당 건물 중의 광이용효율은 유의차가 없었다. 소비전력당 엽생체중을 추가로 비교해보면 320μmol·m-2·s-1 처리구에서만 유의적으 로 낮은 효율을 보였고 이외의 처리구에서는 유의차가 없었기 때문에 생산량이 가장 많은 230μmol·m-2·s-1가 가장 효율적 이었다. 광질 실험에서는 적색광과 청색광의 혼합 비율은 RB 8:2에서 생육량과 광이용효율이 동시에 높게 측정되었고 색 차와 flavonoids 함량에서는 유의차가 발생하지 않아 Red: Blue 비율 8:2가 가장 적합한 조건이었다. 광주기 실험에서는 광주기가 길어질수록 높은 생육량을 나타냈는데 일장 12시간 이상에서는 생육량의 유의차가 없었으므로 광소비 효율을 고 려한 12시간이 적합한 조건이었다. 이상의 결과를 바탕으로 식물공장에서 들깨 생육을 위한 LED 광 환경 조건으로는 광 도, 광질과 일장은 각각 230μmol·m-2·s-1 이상, 8:2와 12시간 이상이었다.

The smart factory is an important system that can reduce defects, maximize productivity, and respond to customer needs, from the labor-intensive era of traditional small and medium-sized manufacturing companies through the automation era to CPS using ICT. However, small and medium-sized manufacturers often fall short of the basic stage due to economic and environmental constraints, and there are many companies that do not even recognize the concept of a smart factory. In this situation, to expand the smart factory of small and medium-sized enterprises, the project to support the establishment of a smart factory for the win-win between large and small enterprises. The win-win smart factory construction support project provides a customized differentiation program support project according to the size and level of the company for all domestic manufacturing SMEs regardless of whether or not they are dealing with Samsung. In this study, we analyze the construction status and introduction performance of companies participating in the win-win smart factory support project to find out whether they have been helpful in management and to find efficient ways to improve support policies, and to suggest the direction of continuous support projects to improve the manufacturing competitiveness of SMEs in the future.

This study examines the effects of participation purpose, corporate readiness, and acceptance of changes that may occur in the course of expert guidance on the performance of smart factory. For this study, 129 questionnaires obtained from SMEs participating in the Smart Meister support project were used, and SPSS 18.0 and the AMOS 18.0 program were used for statistical processing for empirical analysis of the hypotheses test. It was found that the company's business participation motivation and readiness status had a significant effect on the acceptance and cooperation of changes that occurred during the consulting process. In addition, the acceptance and cooperation of changes within the company had a significant effect on the satisfaction with the Meister support project and the financial performance. Companies participating in the Meister support project need to clarify their motives for participating in the project and make stable corporate readiness in advance. In addition, based on the CEO’s support, it is necessary to have a motivational program and to build an organizational culture that can actively accept innovation.

Ppuri or Root technology primarily includes technologies such as casting, mold, plastic working, welding, heat treatment and surface treatment. It is regarded as an essential element for improving the competitiveness of the quality of final products. This study investigates the current status of smart factory implementation for Ppuri companies and analyzes the influencing relationships among various company factors. The factors affecting smart factory implementation for Ppuri companies are sales, exports, number of technical employees, and holding corporate research institutes. In addition, this research shows that even if smart factory implementation is pursued for data collection, data utilization is not implemented properly. Thus, it is suggested that the implementation of smart factories requires not only the availability of facilities and systems but also proper data utilization.