• Journal of Korea Spatial Information System Society
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• v.7 no.3 s.15
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• pp.33-44
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• 2005

Recently, the weather has been influenced by the environmental pollution and the oil price has been risen because of the lack of resources. So, the weather and energy are influencing on not only enterprises or nations, but also individual daily life and economic activities very much. Because of these reasons, there are so many researches about management of solar radiation needed to develope solar energy as alternative energy. And many researchers are also interested in identifying the area according to changing characteristics of climate data. However, the researches have not developed how to apply the cluster analysis, retrieval and analytical results according to the characteristics of the area through data mining. In this paper, we design a data model of the data for storing and managing the climate data tested in twenty cities in the domestic area. And we provide the information according to the characteristics of the area after clustering the domestic climate data, using k-means clustering algorithm. And we suggest the way how to apply the department store and amusement park as an applied weather marketing. The proposed system is useful for constructing the database about the weather marketing and for providing the elements and analysis information.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.389-396
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• 2020

In this paper, a cultivation traceability system to implement smart agriculture developed and implemented, and in particular, devised a system that manages the cultivation traceability of grains that are difficult to grow in smart farms. Mobile and web programs based on smart devices are designed, and the collected information is stored in a DB server and can be used as big data. In addition, real-time location information and agricultural activity information can be matched using an electronic map(Vworld) based on GIS/LBS applying GPS of a mobile device. By designing the cultivation traceability information DB required in the field, the farmhouse, farmers, and cultivation information were developed to make it easy for managers to use, and implemented mobile and web programs in the field. The system is expected to raise the quality and safety management capabilities to the next level in response to variables such as labor saving effect and climate change.

• Journal of the Society of Disaster Information
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• v.17 no.2
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• pp.254-265
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• 2021

Purpose: Inspection and diagnosis on the performance and safety through domestic port facilities have been conducted for over 20 years. However, the long-term development strategies and directions for facility renewal and performance improvement using the diagnosis history and results are not working in realistically. In particular, in the case of port structures with a long service life, there are many problems in terms of safety and functionality due to increasing of the large-sized ships, of port use frequency, and the effects of natural disasters due to climate change. Method: In this study, the maintenance history data of the gravity type quay in element level were collected, defined as big data, and a predictive approximation model was derived to estimate the pattern of deterioration and aging of the facility of project level based on the data. In particular, we compared and proposed models suitable for the use of big data by examining the validity of the state-based deterioration pattern and deterioration approximation model generated through machine learning algorithms of GP and SGP techniques. Result: As a result of reviewing the suitability of the proposed technique, it was considered that the RMSE and R² in GP technique were 0.9854 and 0.0721, and the SGP technique was 0.7246 and 0.2518. Conclusion: This research through machine learning techniques is expected to play an important role in decision-making on investment in port facilities in the future if port facility data collection is continuously performed in the future.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.146-153
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• 2022

Purpose: In the case of domestic port facilities, port structures that have been in use for a long time have many problems in terms of safety performance and functionality due to the enlargement of ships, increased frequency of use, and the effects of natural disasters due to climate change. A big data analysis method was studied to develop an approximate model that can predict the aging pattern of a port facility based on the maintenance history data of the port facility. Method: In this study, member-level maintenance history data for caisson-type quay walls were collected, defined as big data, and based on the data, a predictive approximation model was derived to estimate the aging pattern and deterioration of the facility at the project level. A state-based aging pattern prediction model generated through Gaussian process (GP) and linear interpolation (SLPT) techniques was proposed, and models suitable for big data utilization were compared and proposed through validation. Result: As a result of examining the suitability of the proposed method, the SLPT method has RMSE of 0.9215 and 0.0648, and the predictive model applied with the SLPT method is considered suitable. Conclusion: Through this study, it is expected that the study of predicting performance degradation of big data-based facilities will become an important system in decision-making regarding maintenance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.6
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• pp.764-771
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• 2018

Domestic and foreign agricultural environments nowadays are undergoing various changes such as aging of agricultural population, increase of earned population, rapid climate change, diversification of agricultural product distribution structure, depletion of water resources and limited cultivation area. In order to respond to various environmental changes in recent agriculture, practical use of Smart Greenhouse to easily record, store and manage crop production information such as crop growing information, growth environment and agriculture work log, Interest is growing. In this paper, we propose a system that collects the situation information necessary for growth such as temperature, humidity, solar radiation, CO2 concentration, and monitor the collected data, which can be measured in the rhizosphere of the crop. We have developed a system that collects data such as temperature, humidity, radiation, and growth environment data, which are measured by data obtained from the rhizosphere measuring section of a growing crop and measured by a sensor, and transmitted to a wireless communication gateway of 400 MHz. We developed the integrated SW that can monitor the rhythm environment data and visualize the data by using cloud based data. We can monitor by graph format and data format for visualization of data. The existing smart farm managed crops and facilities using only the data within the farm, and this study suggested the most efficient growth environment by collecting and analyzing the weather and growth environment of the farms nationwide.