• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.83-91
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• 2021

For agricultural products, supply is irregular due to changes in meteorological conditions, and it has high price elasticity. For example, if the supply decreases by 10%, the price increases by 50%. Due to these fluctuations in the prices of agricultural products, the Korean government guarantees the safety of prices to producers through small merchants' auctions. However, when prices plummet due to overproduction, protection measures for producers are insufficient. Therefore, in this paper, we designed a business model that can be used in the electronic transaction system by predicting the price of agricultural products with an artificial intelligence algorithm. To this end, the trained model with the training pattern pairs and a predictive model was designed by applying ARIMA, SARIMA, RNN, and CNN. Finally, the agricultural product forecast price data was classified into short-term forecast and medium-term forecast and verified. As a result of verification, based on 2018 data, the actual price and predicted price showed an accuracy of 91.08%.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.279-286
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• 2021

Recently, the effort of enterprises are visualized to correspond for 4th industrial revolution and climate change. Reaching the operation of industrial facilities are one of these efforts and is actively progressing under identical condition between real and virtual world through introduction of cyber-physical system (CPS). However, the problem on no unified definition for CPS still exists. Thus, in this paper, we review the previous concept of CPS. We propose new concept of CPS with four sections such as real world section, communication section, virtual world section and management section. We also propose definite concept by classifying the layer of each section. In order to confirm the possibility of application for proposed concept of CPS, we applied simple motor. We compare the result for torque between real motor and virtual motor. Finally we confirm that the applicability of proposed concept of CPS is very high.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.5
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• pp.405-412
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• 2022

This paper studies the method of estimating power loss and classifying the factors for improving the power generation efficiency through O&M. It is installed under various climatic conditions worldwide, operational and maintenance technologies suitable for the characteristics of the installation location are required. Existing studies related to solar power generation efficiency have been actively quantifying the impact on short-term losses by environmental factors such as high temperature, dust accumulation, precipitation, humidity, and wind speed, but analysis of the overall impact from a long-term operation perspective is limited. In this study, the potential for efficiency improvement was analyzed by re-establishing a loss classification system according to the power flow of solar power to derive a comprehensive efficiency model for long-term operation and estimating power loss through a case study for each region where climate conditions are classified. As a result of the analysis, the average annual potential for improving soiling loss was 26.9%, Death Valley 7.2%, and Seoul 3.8%. Aging losses was 6.6% in the 20th year as a cumulative. The average annual potential due to temperature loss was 2.9 % for Doha, 1.9% for Death Valley, and 0.2% for Seoul.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.489-498
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• 2023

Advancements in hardware performance and computing technology have facilitated the progress of climate prediction models to address climate change. The Korea Meteorological Administration employs the GloSea6 model with supercomputer technology for operational use. Various universities and research institutions utilize the Low-GloSea6 model, a low-resolution coupled model, on small to medium-scale servers for weather research. This paper presents an analysis using Intel VTune Profiler on Low-GloSea6 to facilitate smooth weather research on small to medium-scale servers. The tri_sor_dp_dp function of the atmospheric model, taking 1125.987 seconds of CPU time, is identified as a hotspot. Nonlinear regression models, a machine learning technique, are applied and compared to existing functions conducting numerical operations. The K-Nearest Neighbors regression model exhibits superior performance with MAE of 1.3637e-08 and SMAPE of 123.2707%. Additionally, the Light Gradient Boosting Machine regression model demonstrates the best performance with an RMSE of 2.8453e-08. Therefore, it is confirmed that applying a nonlinear regression model to the tri_sor_dp_dp function during the execution of Low-GloSea6 could be a viable alternative.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1143-1150
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• 2023

The global climate crisis and the implementation of low-carbon policies have led to a growing interest in renewable energy and a growing number of related industries. Among them, solar power is attracting attention as a representative eco-friendly energy that does not deplete and does not emit pollutants or greenhouse gases. As a result, the supplement of solar power facility is increasing all over the world. However, solar power is easily affected by the environment such as geography and weather, so accurate solar power forecast is important for stable operation and efficient management. However, it is very hard to predict the exact amount of solar power using statistical methods. In addition, the conventional prediction methods have focused on only short- or long-term prediction, which causes to take long time to obtain various prediction models with different prediction horizons. Therefore, this study utilizes a many-to-many structure of a recurrent neural network (RNN) to integrate short-term and long-term predictions of solar power generation. We compare various RNN-based very short- and long-term prediction methods for solar power in terms of MSE and R² values.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.1
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• pp.105-110
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• 2014

Korea, old buildings do not currently have earth-resistant facilities, and disaster prevention facilities have some limitation in responding properly to damage arising from natural disaster as they do not reflect recently occurring climatic changes. Accordingly, it is necessary to select vulnerable districts by taking into consideration the possibility of disaster occurrence and the current situation of infrastructures for disaster prevention in order to responding efficiently to future unexpected disasters. For this purpose, national government has recently formed the DB linkage council for disaster prevention under the participation of related agencies and constructed integrated database. But local governments have not properly provided it with basic data necessary to construct database, resulting in the delay of the project. Therefore, it is thought that national government needs to provide them with manpower and financial support.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.1
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• pp.21-30
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• 2020

In this paper, we predicted the propagation loss for the air-to-ground (A2G) channel between the ground control system and the unmanned aerial vehicle (UAV) using the prediction model for the aircraft recommended by the International Telecommunication Union (ITU). We analyzed the network coverage of the aerial relay system based on the medium altitude UAVs by expanding it into the air-to-air (A2A) channel. Climate and geographic factors in Korea were used to predict propagation loss due to atmospheres. We used the measured data published by the Telecommunication Technology Association (TTA) for regional rainfall-rate and effective earth radius factors to increase accuracy. In addition, the aerial relay communication system used the key parameter of the common data link (CDL) system developed in Korea recently. Prediction results show that the network coverage of the aerial relay system broadens at higher altitude.

• The Journal of Society for e-Business Studies
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• v.25 no.3
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• pp.77-93
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• 2020

The literature has reported that hierarchical classification methods generally outperform the flat classification methods for a multi-class document classification problem. Unlike the literature that has constructed a class hierarchy, this paper evaluates the performance of hierarchical and flat classification methods under a situation where the class hierarchy is predefined. We conducted numerical evaluations for two data sets; research papers on climate change adaptation technologies in water sector and 20NewsGroup open data set. The evaluation results show that the hierarchical classification method outperforms the flat classification methods under a certain condition, which differs from the literature. The performance of hierarchical classification method over flat classification method depends on class similarities at levels in the class structure. More importantly, the hierarchical classification method works better when the upper level similarity is less that the lower level similarity.

• The Transactions of the Korean Institute of Power Electronics
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• v.11 no.1
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• pp.79-89
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• 2006

Particularly the photovoltaic systems are preferred because the output is extracted to the useful electric energy. However, the output characteristics of photovoltaic(PV) systems using solar cell or array depend on the weather conditions. The assistant equipment which emulates the solar cell characteristics that can be controlled arbitrarily by researcher is required to the researchers for reliable experimental data. To solve these problems, it is necessary to research a solar cell model of which output characteristics varied by setting the weather conditions such as insolation levels and temperatures. Therefore, this paper was presented that improved model which is based on interpolation model. To verified the improved model, it is confirmed using the simulation of MATLAB. Also, the experiment was performed by the characteristics of virtual implemented solar cell(VISC) system with the proposed solar cell model. It could be confirmed that there exists actual ewer within 5% between actual solar cell and VISC system.

• Journal of IKEEE
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• v.18 no.1
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• pp.45-56
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• 2014

Recently, damages from disasters such as downpours, earthquakes and typhoons are increasing throughout the world. The downpour days of Korean Peninsula are also increasing every year due to rapid climate change. According to statistics over the last 30 years of the earthquakes in Korean Peninsula, the probability of a future earthquake is very high. In addition, super typhoons will hit Korean Peninsula due to the temperature rise in the nearby sea caused by the deepening of global warming. Thus, damage costs of the waterfront structures by natural disasters are also growing. But damage information system for integrated management of waterfront structures are insufficient. In this paper, we designed and implemented a damage information system for integrated management of waterfront structures. First, we classified damage information caused by natural disaster. Then we designed the databases of damage information and implemented damage information system. Lastly, we checked operations and the feasibility by testing queries on the proposed system.