Agrophotovoltaic (APV) system is an integrated system producing crops as well as solar energy. Because crop production underneath Photovoltaic (PV) modules requires delicate management of crops, smart farming equipment such as real-time remote monitoring sensors (e.g., soil moisture sensors) and micro-climate monitoring sensors (e.g., thermometers and irradiance sensors) is installed in the APV system. This study aims at introducing a decision support system (DSS) for smart farming in an APV system. The proposed DSS is devised to provide a mobile application service, satellite image processing, real-time data monitoring, and performance estimation. Particularly, the real-time monitoring data is used as an input of the DSS system for performance estimation of an APV system in terms of production yields of crops and monetary benefit so that a data-driven function is implemented in the proposed system. The proposed DSS is validated with field data collected from an actual APV system at the Jeollanamdo Agricultural Research and Extension Services in South Korea. As a result, farmers and engineers enable to efficiently produce solar energy without causing harmful impact on regular crop production underneath PV modules. In addition, the proposed system will contribute to enhancement of the smart farming technology in the field of agriculture.

Agrophotovoltaic (APV) system is an integrated system producing crops as well as solar energy. Because crop production underneath Photovoltaic (PV) modules requires delicate management of crops, smart farming equipment such as real-time remote monitoring sensors (e.g., thermometers, irradiance sensors, and soil moisture sensors) is installed in the APV system. This study aims at introducing a simulation-based decision support system (DSS) for smart farming in an APV system. The proposed DSS is devised to provide a mobile application service, satellite image processing, real-time data monitoring, and simulation-based performance estimation. Particularly, an agent-based simulation (ABS) is used to mimic functions of an APV system so that a data-driven function and digital twin environment are implemented in the proposed system. The ABS model is validated with field data collected from an actual APV system at the Jeollanamdo Agricultural Research and Extension Services in South Korea. As a result, farmers and engineers enable to efficiently produce solar energy without causing harmful impact on regular crop production underneath PV modules. In addition, the proposed system will contribute to enhancement of the digital twin technology in the field of agriculture.

사회적인 존재로서 우리는 다른 사람의 행위를 빠르고 정확히 이해할 필요가 있다. 행위 이해는 여러 수준에서 일어난다. 무심코 타인의 의도를 알아챌 때가 있는가 하면, 그들의 목적을 추론하기 위해 노력해야만 하는 경우도 있다. 결합 사이먼 효과는 과제를 분업하는 한 쌍의 참가자가 의도치 않게 동료의 행위를 표상할 수 있음을 실험적으 로 증명한다. 이 효과는 거의 자동적으로 발생한다고 알려졌지만, 인지부하의 영향을 받지 않을 만큼 자동적인지는 확인되지 않았다. 이에 본 연구는 참가자에게 작업기억 부하가 있거나 없는 상태에서 결합 사이먼 과제를 수행하게 하였다. 이중과제 구획에서 저부하 집단의 참가자들은 한 개의 숫자를 작업기억에 유지한 채로 사이먼 과제를 분업 하였고, 고부하 집단의 참가자들은 다섯 개의 숫자를 유지한 채로 사이먼 과제를 분업하였다. 작업기억 부하의 효과 를 분석하기 위해 이중과제 구획과 단일과제 구획의 집단별 사이먼 효과를 비교하였다. 반응시간을 분석한 결과, 저부하 집단은 이중과제와 단일과제 구획에서 모두 사이먼 효과를 보였지만, 고부하 집단은 어느 과제 구획에서도 사이먼 효과를 보이지 않았다. 이 결과는 결합 사이먼 효과가 인지 자원에 의존한다는 면에서 자동적인 현상을 아님 을 의미한다. 즉, 분업 참여자는 인지적 자원이 가용한 경우에만 동료의 행위를 과제 표상에 반영하는 것으로 보인다.

The present study was conducted from August to December 2016 in a cylindrical water tank with a diameter of 1 m, a height of 4 m and a capacity of 3,000 L. The field water and sediment from the Nakdong River were also sampled for the experimental culture (field water+sediment) and control culture (field water), respectively. In this study, we aimed to investigate phytoplankton succession pattern using the phytoplankton functional group in the enclosed culture system. A total of 50 species in 27 genera including Chlorophyceae (30 species), Bacillariophyceae (11 species), Cyanophyceae (7 species), and Cryptophyceae (2 species) were identified in the experimental and control culture systems. A total of 19 phytoplankton functional groups (PFGs) were identified, and these groups include B, C, D, F, G, H1, J, K, Lo, M, MP, N, P, S1, TB, W0, X1, X2 and Y. In particular, W0, J and M groups exhibited the marked succession in the experimental culture system with higher biovolumes compared to those of the control culture system, which may be related to the internal cycling of nutrients by sediment in the experimental culture system. The principal component analyses demonstrated that succession patterns in PFG were associated with the main environmental factors such as nutrients (N, P), water temperature and light intensity in two culture systems. In conclusion, the present study showed the potential applicability of the functional group for understanding the adaptation strategies and ecological traits of the phytoplankton succession in the water bodies of Korea.

Overwintering and succession of phytoplankton community with physicochemical and biological characteristics were investigated in pilot culture system. Water and phytoplankton samples were collected twice a week from February 23 to June 28, 2016. A total of 17 overwintering taxa including cyanophyceae, chlorophyceae, bacillariophyceae were identified in the experimental group in winter (February), and these overwintering species showed a marked succession pattern along with environment changes. In the process of phytoplankton succession, a total of 56 species in 28 genera were identified in two (experimental, control) pilot culture system. In the experimental group, 52 phytoplankton species in 24 genera were identified, and the number of taxa was highest in chlorophyceae (35 species), followed by Bacillariophyceae (9 species), Cyanophyceae (5 species) and others (3 species). In the control group, 25 phytoplankton species in 14 genera were classified and these taxa consisted of 17 chlorophyceae, 3 cyanophyceae, 2 Bacillariophyceae and 3 others. The standing crops ranged from 40 to 325,450 cells mL-1 in the experimental group, and from 900 to 37,100 cells mL-1 in the control group, respectively. The dominant species were represented by Monoraphidium minutum, Microcystis aeruginosa, Rhodomonas lacustris, Ankyra judai and Chlorella vulgaris in the experimental group; and M. minutum and Coenochloris cf. pyrenoidosa in the control group. In conclusion, overwintering and succession of predominant phytoplankton species developed due to interactions of internal environmental factors (physicochemical and biological factors) in the pilot culture system.