Yong-Yun Cho

A Smart Service Model Based on Ubiquitous Sensor Networks Using Vertical Farm Ontology

Application of the technology systems is growing in various fields and the agriculture is not an exception. Agriculture is also reaping the benefits of technological innovation which helps in quantitative and qualitative food production. Vertical farm, one of the agricultural practices, is considered to be the future of agriculture with the rate of population migrating into urban areas. Ubiquitous computing in agriculture is emerging remarkably in this fast processing pervasive environment, owing to wireless sensor network (WSN). Building a context aware system for the vertical farm is complex without the semantic interoperability between the Internet of things (IOT). In this paper, we propose a vertical farm ontology (VFO), an OWL based ontology model which helps in more understanding of the relationship between the domain factors. With the proposed model, the information from the Internet of things is recomposed as context information and made understandable for the other systems. For the sake of agriculture, we hope that our proposed model will pave great path for the development of smart and intelligent agricultural services.

An Approach for a Self-Growing Agricultural Knowledge Cloud in Smart Agriculture

Typically, most of the agricultural works have to consider not only fixed data related with a cultivated crop, but also various environmental factors which are dynamically changed. Therefore, a farmer has to consider readjust the fixed data according to the environmental conditions in order to cultivate a crop in optimized growth environments. However, because the readjustment is delicate and complicated, it is difficult for user to by hand on a case by case. To solve the limitations, this paper introduces an approach for self-growing agricultural knowledge cloud in smart agriculture. The self-growing agricultural knowledge cloud can offer a user or a smart agricultural service system the optimized growth information customized for a specific crop with not only the knowledge and the experience of skillful agricultural experts, but also useful analysis data, and accumulated statistics. Therefore, by using the self-growing agricultural knowledge cloud, a user can easily cultivate any crop without a lot of the crop growth information and expert knowledge.

Implementation of the Closed Plant Factory System Based on Crop Growth Model

The paper proposed the Closed Plant Factory System (CPFS) applied the crop growth model. The CPFS monitors climate data in a closed building or room and the actuator’s status for control devices, and provides optimized operations for controlling growth environments. The CPFS monitors environmental data, plant growth data and the control devices’ status data. This system can analyse the optimal growth environment and the correct control environment. We implemented the system and applied it to a testbed, also confirmed that the CPFS operated real-time monitoring service and controlling service correctly.

A Study on the Location-Based Reservation Management Service Model Using a Smart Phone

This paper suggests a location-based reservation management service model that can manage various reservation services and provide related information to users through their smart phones in real-time. The proposed service model is based on a smart phone to relieve inconvenience of existing reservation systems that have limited access effectiveness in space and time or long waiting time. To improve service satisfaction, the proposed service model includes a service server to manage highly qualified and reliable reservation schedules with user’s location information from smart phones, and a client to support users to make their application plans through intuitive user interfaces. Therefore, the proposed model can help users to save a long waiting time in various service places, for example food stores, banks, and government office customer service centers.

The Simulation Study on Harvested Power in Synchronized Switch Harvesting on Inductor

Different piezoelectric harvester interface circuits are demonstrated and compared through SPICE simulation. The simulations of the effect of switch triggering offset and switch on time duration on SSHI’s power are performed. The inductor’s quality factors in synchronized switch harvesting on inductor interface have important effect on the harvested power. Parallel SSHI shows the optimal output voltage to harvest the maximum power varies according to the Q severely. It is concluded that switch triggering offset has more impact on the s-SSHI than p-SSHI and the switch on-time duration is more important in case of the p-SSHI. p-SSHI shows when the on-time duration becomes more than 1.3 times or less than 0.7 times of exact duration time, the harvested power gets negligible. s-SSHI reveals the characteristics that when less than 1.5 times exact on-time duration, the harvested power varies significantly with the on-time duration.

A Recommend Service Based on Expert Knowledge Model in Agricultural Environments

Many of works in fields of agriculture for a long time have been based on agricultural experiences. Recently, rapid and unpredictable global climate changes and environmental pollution have made stable agricultural works, difficult. So, for a precision and smart agriculture, agricultural works have to be based on more complicated information and data, but simple experimental know-how in agricultural environments. In this paper, we propose a recommend service based on an agricultural expert knowledge model to support a precision agriculture with Internet, Sensors, and IT. The proposed service can automatically fuse valuable knowledge and experience in the agricultural fields according to experimental changes in the growth of crops, and can recommend best services to farmers. Therefore, with the proposed service, a farmer can easily and stably do agricultural works, irrespective of changes of crops and environmental changes.

Towards a Context Modeling for a Greenhouse Based on USN

Recently more than ever, because of further global warming, violent climate changes, environmental pollution and food problem researchers have taken a lot of interests in greenhouses and vertical farms in agricultural environments. Generally, works in greenhouses are based on situation information from various sensors based on USN. In this paper, we propose a context modeling method for a lot of situation information which can arise in agricultural environments. The suggested context modeling method can define a various data generated in greenhouses based on USN as structural contexts based on a rule-based RDF. Through the suggested modeling method, various sensed data in a greenhouse can be regenerated as high-level context sets. Because the high-level contexts can be usefully used as important service execution conditions, the suggested context modeling method can be widely applied to various smart agricultural service applications or platforms for greenhouses based on USN and IT technologies and raise efficiency in development of a context-aware system and a context-based service automation system.