APV systems producing both crops and electricity are becoming popular as an alternative way of producing renewable energy in many countries with land shortage issues (e.g., South Korea). This study aims at developing a hybrid performance model of an Agrophotovoltaic (APV) system that produces crops underneath the PV modules. In this study, the physical model used to estimate solar radiation is integrated with a polynomial regression approach to forecast the amount of electricity generation and crop production in the APV system. The model takes into account not only the environmental factors (i.e., daily temperature, precipitation, humidity, and wind speed) but also physical factors (i.e., shading ratio of the APV system) related to the performance of the APV system. For more accurate modelling, the proposed approach is validated based on field experiment data collected from the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. As a result, the proposed approach can predict the electricity generation quantity in the APV system with an R² of 80.4%. This will contribute to the distribution of the APV system, which will increase farmers’ income as well as the sustainability of our society.

Agrophotovoltaic (APV) systems produce both solar energy and crops, so they are considered a sustainable alternative to traditional solar power plants, which can potentially destroy farmlands. However, it is challenging to diffuse APV systems because of their high installation and operating costs. Thus, to resolve the issue by maximizing the productivity and profits of an APV system, this study aims to propose a mobile-phone-based decision support system (DSS) for a supply chain network design for APV systems in South Korea using satellite imagery incorporating geographic information system (GIS) data. Particularly, polynomial regression models estimating annual corn (Zea mays) yields and the predicted generation of electricity were developed and integrated with the proposed DSS. Field experiment data provided by the APV system at Jeollanamdo Agricultural Research and Extension Services in South Korea were utilized. Two photovoltaic (PV) module types (mono-facial and bi-facial) and three different shading ratios for APV systems (21.3%, 25.6%, and 32.0%) were considered design factors for APV systems. An optimal network structure of 6 candidate APV systems and 15 agricultural markets was devised using the generalized reduced gradient (GRG) method. The profits of the six candidate APV systems are mainly affected by the transportation costs to the markets and the policy of the electricity selling prices. As a result, the proposed supply chain design framework successfully identifies an APV system network with maximum profits from crop production as well as electricity generation.

Although articulated robots with flexible automation systems are essential for implementing smart factories, their high initial investment costs make them difficult for small and medium-sized enterprises to implement. This study proposes a federated learning-based articulated robot control framework to improve the task completion of multiple articulated robots used in automated systems under limited computing resources. The proposed framework consists of two modules: (1) a federated learning module for the cooperative training of multiple joint robots on a part-picking task and (2) an articulated robot control module to balance the efficiency of limited resources. The proposed framework is applied to cases with different numbers of joint robots, and its performance is evaluated in terms of training completion time, resource share ratio, network traffic, and completion time of a picking task. Under the devised framework, the experiment demonstrates object recognition by three joint robots with an accuracy of approximately 80% at a minimum number of learning rounds of 76 and with a network traffic intensity of 2303.5 MB. As a result, this study contributes to the expansion of federated learning use for articulated robot control in limited environments, such as small and medium-sized enterprises.

The global healthcare market is expanding, with a particular focus on personalized care for individuals who are unable to leave their homes due to the COVID-19 pandemic. However, the implementation of personalized care is challenging due to the need for additional devices, such as smartwatches and wearable trackers. This study aims to develop a human body simulation that predicts and visualizes an individual’s 3D body changes based on 2D images taken by a portable device. The simulation proposed in this study uses semantic segmentation and image-based reconstruction techniques to preprocess 2D images and construct 3D body models. It also considers the user’s exercise plan to enable the visualization of 3D body changes. The proposed simulation was developed based on human-in-the-loop experimental results and literature data. The experiment shows that there is no statistical difference between the simulated body and actual anthropometric measurement with a p-value of 0.3483 in the paired t-test. The proposed simulation provides an accurate and efficient estimation of the human body in a 3D environment, without the need for expensive equipment such as a 3D scanner or scanning uniform, unlike the existing anthropometry approach. This can promote preventive treatment for individuals who lack access to healthcare.