• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.361-362
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• 2015

This paper proposes a Direct Power Control (DPC) scheme of improved command tracking capability for Permanent Magnet Synchronous Generator (PMSG) Medium Voltage (MV) Wind Turbines. Benchmarking is performed based on a neutral point clamped three-level back-to-back type voltage source converter. It is introduced to design the DPC modeling and propose DPC scheme of a three-level NPC (3L-NPC) converter. During the fault condition in wind farms, the proposed control scheme directly controls the generated output power to the command value from the hierarchical wind farm controller. The proposed control scheme is compared with conventional control scheme as respect to loss and thermal analysis. The DPC scheme of improved command tracking capability is confirmed through PLECS simulations. Simulation result shows that proposed control scheme achieves a much shorter transient time in a step response of generated output power. The proposed control scheme makes it possible to provide a good dynamic performance for PMSG MV wind turbine to generate a high quality output power under grid fault condition.

• Smart Media Journal
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• v.7 no.4
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• pp.37-43
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• 2018

Most of fruit quality classification has been done by time consuming, inaccurate and intensive manual labor. This study proposed an automated fruit grading system based on appearances and internal flavors. In this study, image processing technique and a weight checker were used to measure the value of appearance features and the near infrared spectroscopy analysis method was used to estimate the value of internal flavors. Additionally, I suggested 8x8x5x5 ANN based fruit quality classifier model to grade fruits quality. The proposed automated fruit quality classification system is expected to be very beneficial for many farms where heavy manual labor is usually needed for fruit quality classification.

• Smart Media Journal
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• v.12 no.3
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• pp.112-119
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• 2023

Shrimp farming has been becoming a new source of income for fishermen in South Korea. It is often necessary for fishers to measure the size of the shrimp for the purpose to understand the growth rate of the shrimp and to determine the amount of food put into the breeding pond. Traditional methods rely on humans, which has huge time and labor costs. This paper proposes a deep learning-based method for calculating the size of shrimps automatically. Firstly, we use fine-tuning techniques to update the Mask RCNN model with our farm data, enabling it to segment shrimps and generate shrimp masks. We then use skeletonizing method and maximum inscribed circle to calculate the length and width of shrimp, respectively. Our method is simple yet effective, and most importantly, it requires a small hardware resource and is easy to deploy to shrimp farms.

• Smart Media Journal
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• v.12 no.10
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• pp.9-18
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• 2023

Harvest forecasting is the great demand of multiple aspects like temperature, rain, environment, and their relations. The existing study investigates the climate conditions and aids the cultivators to know the harvest yields before planting in farms. The proposed study uses federated learning. In addition, the additional widespread techniques such as bagging classifier, extra tees classifier, linear discriminant analysis classifier, quadratic discriminant analysis classifier, stochastic gradient boosting classifier, blending models, random forest regressor, and AdaBoost are utilized together. These presented nine algorithms achieved exemplary satisfactory accuracies. The powerful contributions of proposed algorithms can create exact harvest forecasting. Ultimately, we intend to compare our study with the earlier research's results.

• Smart Media Journal
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• v.13 no.2
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• pp.23-31
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• 2024

In precision cattle farm, reliably tracking the identity of each cattle is necessary. Effective tracking of cattle within farm environments presents a unique challenge, particularly with the need to minimize the occurrence of excessive tracking trajectories. To address this, we introduce a trajectory playback decision tree algorithm that reevaluates and cleans tracking results based on spatio-temporal relationships among trajectories. This approach considers trajectory as metadata, resulting in more realistic and accurate tracking outcomes. This algorithm showcases its robustness and capability through extensive comparisons with popular tracking models, consistently demonstrating the promotion of performance across various evaluation metrics that is HOTA, AssA, and IDF1 achieve 68.81%, 79.31%, and 84.81%.

• Smart Media Journal
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• v.1 no.2
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• pp.47-53
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• 2012

Wind energy is currently the fastest growing source of renewable energy used for electrical generation around world. Wind farms are adding a significant amount of electrical generation capacity. The increase in the number of wind farms has led to the need for more effective operation and maintenance procedures. Condition Monitoring System(CMS) can be used to aid plant owners in achieving these goals. In this work, systematic design procedure for artificial neural network based normal behavior model which can be applied for fault detection of various devices is proposed. Furthermore, to verify the design method SCADA(Supervisor Control and Data Acquisition) data from 850kW wind turbine system installed in Beaung port were utilized.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.4
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• pp.695-702
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• 2020

Recently, as research on smart farms has been actively conducted, indoor environment control, such as a green house, has reached a high level. However, In the field of forestry where cultivation is carried out in outdoor, the use of ICT is still insufficient. In this paper, we propose LPWA-based forest growth environment collection and big data analysis system using ICT technology. The proposed system collects and transmits the field cultivation environment data to the server using small solar power generation and LPWA technology based on the oneM2M architecture. The transmitted data is constructed as big data on the server and utilizes it to predict the production and quality of forest products. The proposed system is expected to contribute to the production of low-cost, high-quality crops through the fusion of renewable energy and smart farms. In addition, it can be applied to other industrial fields that utilize the oneM2M architecture and monitoring the growth environment of agricultural crops in the field.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.4
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• pp.161-173
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• 2020

As ICT convergence technology has spread and applied to various industrial fields and society in general, interest in rural entrepreneurship using smart farm as a means for solving many pending problems in agriculture is increasing. In this context, this study is to look at the influential factors in terms of perceived usefulness associated with the rural entrepreneurial intention using smart farm and suggest a proposal for spreading smart farms. The subjects were 296 general adults over 20 years old who were selected by simple random sampling method. The research method was exploratory factor analysis and multiple regression analysis using IBM SPSS 22.0. The perceived usefulness of smart farm, which are availability, reliability and economic efficiency were selected as independent variables to analyze the influential factors on rural entrepreneurial intention using smart farm and the moderating effect of personal innovation was observed. As a result, reliability and economic efficiency have a positive(+) influence on rural entrepreneurial intention using smart farm. And personal innovation moderates the relationship between the availability, reliability of smart farm and rural entrepreneurial intention using smart farm. The results of this study have significance in that we devised and empirically revealed factors affecting rural entrepreneurship intentions from the perspective of perceived usefulness of smart farms, away from studies of general entrepreneurship intention factors such as internal personal characteristics and external environmental factors. The implications of the study are expected to be utilized at the seeking direction of policy for potential entrepreneur using smart farm, the training and consulting in actual field of smart farm.

• Journal of Convergence for Information Technology
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• v.10 no.2
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• pp.15-20
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• 2020

In this paper, we propose an ICT-based smart farm design. At present, the decrease in rural population is naturally inevitable due to the decrease of the total population. The economic burden on each farm grows with increasing labor costs. As a solution to this, the necessity of spreading smart farms using computing resources is emerging. The proposed system utilizes the ICT technology emerging from the Fourth Industrial Revolution. We will use big data analysis to collect a large amount of data and propose a platform for managing collected data and providing efficient services. The proposed platform consists of SOA service layer, middleware layer, resource pool layer and physical resource layer. ICT-based smart farm service can reduce costs and be easy to install and manage because ICT-based smart farm service provides only necessary functions from the user's point of view.

• Smart Media Journal
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• v.5 no.3
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• pp.17-24
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• 2016

Korean agriculture is currently experiencing difficulties as a cause of rural depopulation, aging of rural population, grain self-sufficiency rate decline, and deepening of climate change. It is necessary to ensure our country's agriculture industrial competitiveness in accordance with opening of FTA imports expanded. To ensure the underdeveloped competitive, Korean government defines the 3rd generation model from 1st generation model to extend the smart farms of Korean types. The agriculture smarting overcomes the growth limitations of agriculture, and efforts to develop 6th + ${\alpha}$ industry. In this paper, We define and verify the IoF(Internet of Farming)-Cloud based substantial services about 2rd generation model, and propose a greenhouse of IoF-Cloud testbed.