• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1217-1227
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• 2018

This paper aims to evaluate the applicability of dam inflow prediction model using recurrent neural network theory. To achieve this goal, the Artificial Neural Network (ANN) model and the Elman Recurrent Neural Network(RNN) model were applied to hydro-meteorological data sets for the Soyanggang dam and the Chungju dam basin during dam operation period. For the model training, inflow, rainfall, temperature, sunshine duration, wind speed were used as input data and daily inflow of dam for 10 days were used for output data. The verification was carried out through dam inflow prediction between July, 2016 and June, 2018. The results showed that there was no significant difference in prediction performance between ANN model and the Elman RNN model in the Soyanggang dam basin but the prediction results of the Elman RNN model are comparatively superior to those of the ANN model in the Chungju dam basin. Consequently, the Elman RNN prediction performance is expected to be similar to or better than the ANN model. The prediction performance of Elman RNN was notable during the low dam inflow period. The performance of the multiple hidden layer structure of Elman RNN looks more effective in prediction than that of a single hidden layer structure.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.4
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• pp.23-32
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• 2019

Increasing crop production with the same amount of resources is essential for enhancing the economy in agriculture. The first prerequisite is to understand relationships between the resources. The concept of WEF (Water-Energy-Food) nexus analysis was first introduced in 2011, which helps to interpret inter-linkages among the resources and stakeholders. The objective of this study was to analyze energy-water nexus in greenhouse cultivation by estimating reference evapotranspiration and heating load. For the estimation, this study used the physical model to simulate the inside temperature of the agricultural greenhouse using heating, solar radiation, ventilated and transferred heat losses as input variables. For estimating reference evapotranspiration and heating load, Penman-Monteith equation and seasonal heating load equation with HDH (Heating Degree-Hour) was applied. For calibration and validation of simulated inside temperature, used were hourly data observed from 2011 to 2012 in multi-span greenhouse. Results of the simulation were evaluated using $R^2$, MAE and RMSE, which showed 0.75, 2.22, 3.08 for calibration and 0.71, 2.39, 3.35 for validation respectively. When minimum setting temperature was $12^{\circ}C$ from 2013 to 2017, mean values of evapotranspiration and heating load were 687 mm/year and 2,147 GJ/year. For $18^{\circ}C$, Mean values of evapotranspiration and heating load were 707 mm/year and 5,616 GJ/year. From the estimation, the relationship between water and heat energy was estimated as 1.0~2.6 GJ/ton. Though additional calibrations with different types of greenhouses are necessary, the results of this study imply that they are applicable when evaluating resource relationship in the greenhouse cultivation complex.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.451-461
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• 2019

The dam reservoir inflow prediction is utilized to ensure for water supply and prevent future droughts. In this study, we predicted the dam reservoir inflow and analyzed how seasonal weather forecasting affected the accuracy of the inflow for even multi-purpose dams. The hindcast and forecast of GloSea5 from KMA were used as input for rainfall-runoff models. TANK, ABCD, K-DRUM and PRMS models which have individual characteristics were applied to simulate inflow prediction. The dam reservoir inflow prediction was assessed for the periods of 1996~2009 and 2015~2016 for the hindcast and forecast respectively. The results of assessment showed that the inflow prediction was underestimated by comparing with the observed inflow. If rainfall-runoff models were calibrated appropriately, the characteristics of the models were not vital for accuracy of the inflow prediction. However the accuracy of seasonal weather forecasting, especially precipitation data is highly connected to the accuracy of the dam inflow prediction. It is recommended to consider underestimation of the inflow prediction when it is used for operations. Futhermore, for accuracy enhancement of the predicted dam inflow, it is more effective to focus on improving a seasonal weather forecasting rather than a rainfall-runoff model.

• Journal of Climate Change Research
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• v.1 no.2
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• pp.147-161
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• 2010

The temporal and spatial distributions of surface solar radiation were calculated by the one layer solar radiative transfer model(GWNU) which was corrected by multi layer Line-by-Line(LBL) model during 2009 in South Korea. The aerosol optical thickness, ozone amount, cloud fraction and total precipitable water were used as the input data for GWNU model run and they were retrieved from Moderate Resolution Imaging Spectrometer(MODIS), Ozone Monitoring Instrument(OMI), MTSAT-1R satellite data and the Regional Data Assimilation Prediction System(RDAPS) model result, respectively. The surface solar radiation was calculated with 4 km spatial resolution in South Korea region using the GWNU model and the results were compared with surface measurement(by pyranometer) data of 22 KMA solar sites. The maximum values(more than $5,400MJ/m^2$) of model calculated annual solar radiation were found in Andong, Daegu and Jinju regions and these results were corresponded with the MTSAT-1R cloud amount data. However, the spatial distribution of surface measurement data was comparatively different from the model calculation because of the insufficient correction and management problems for the sites instruments(pyranometer).

• Smart Media Journal
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• v.8 no.3
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• pp.23-30
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• 2019

With the popularization of the Internet and smartphone uses, people in the modern era are living in a multi-channel environment in which they access the information system freely through various methods and media. In the process of utilizing such services, users must authenticate themselves, the typical of which is ID & password authentication. It is considered the most convenient method as it can be authenticated only through the keyboard after remembering its own credentials. On the other hand, modern web services only allow passwords to be set with high complexity by different combinations. Passwords consisting of these complex strings also increase proportionally, since the more services users want to use, the more user authentication information they need to remember is recommended periodically to prevent personal information leakage. It is difficult for the blind, the disabled, or the elderly to remember the authentication information of users with such high entropy values and to use it through keyboard input. Therefore, this paper proposes a user authentication method using Google Assistant, MFCC and DTW algorithms and speaker authentication to provide the handicapped users with an easy user authentication method in the login process.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.161-170
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• 2019

Multi-product mass production small and medium enterprise factories have a wide variety of products and a large number of products, wasting manpower and expenses for inventory management. In addition, there is no way to check the status of inventory in real time, and it is suffering economic damage due to excess inventory and shortage of stock. There are many ways to build a real-time data collection environment, but most of them are difficult to afford for small and medium-sized companies. Therefore, smart factories of small and medium enterprises are faced with difficult reality and it is hard to find appropriate countermeasures. In this paper, we implemented the contents of extension of existing inventory management method through character extraction on label with barcode and QR code, which are widely adopted as current product management technology, and evaluated the effect. Technically, through preprocessing using OpenCV for automatic recognition and classification of stock labels and barcodes, which is a method for managing input and output of existing products through computer image processing, and OCR (Optical Character Recognition) function of Google vision API. And it is designed to recognize the barcode through Zbar. We propose a method to manage inventory by real-time image recognition through Raspberry Pi without using expensive equipment.

• Journal of the Society of Disaster Information
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• v.14 no.4
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• pp.501-509
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• 2018

Purpose: UAV-based photo measurements are being researched using UAVs in the space information field as they are not only cost-effective compared to conventional aerial imaging but also easy to obtain high-resolution data on desired time and location. In this study, the UAV-based high-resolution images were used to perform the land cover classification. Method: RGB cameras were used to obtain high-resolution images, and in addition, multi-distribution cameras were used to photograph the same regions in order to accurately classify the feeding areas. Finally, Land cover classification was carried out for a total of seven classes using created ortho image by RGB and multispectral camera, DSM(Digital Surface Model), NDVI(Normalized Difference Vegetation Index), GLCM(Gray-Level Co-occurrence Matrix) using RF (Random Forest), a representative supervisory classification system. Results: To assess the accuracy of the classification, an accuracy assessment based on the error matrix was conducted, and the accuracy assessment results were verified that the proposed method could effectively classify classes in the region by comparing with the supervisory results using RGB images only. Conclusion: In case of adding orthoimage, multispectral image, NDVI and GLCM proposed in this study, accuracy was higher than that of conventional orthoimage. Future research will attempt to improve classification accuracy through the development of additional input data.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.6
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• pp.385-391
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• 2022

This paper presents to develop a path planning algorithm combining gradient descent-based path planning (GBPP) and particle swarm optimization (PSO) for considering prohibited flight areas, terrain information, and characteristics of fixed-wing unmmaned aerial vehicle (UAV) in 3D space. Path can be generated fast using GBPP, but it is often happened that an unsafe path can be generated by converging to a local minimum depending on the initial path. Bio-inspired swarm intelligence algorithms, such as Genetic algorithm (GA) and PSO, can avoid the local minima problem by sampling several paths. However, if the number of optimal variable increases due to an increase in the number of UAVs and waypoints, it requires heavy computation time and efforts due to increasing the number of particles accordingly. To solve the disadvantages of the two algorithms, hierarchical path planning algorithm associated with hierarchical particle swarm optimization (HPSO) is developed by defining the initial path, which is the input of GBPP, as two variables including particles variables. Feasibility of the proposed algorithm is verified by software-in-the-loop simulation (SILS) of flight control computer (FCC) for UAVs.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.123-141
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• 2023

A sufficient amount of data with quality is needed for training artificial neural networks (ANNs). However, developing ANN models with a small amount of data often appears in engineering fields. This paper presented an ANN model to improve prediction performance of the ammonia emission amount with 83 data. The ammonia emission rate included eleven inputs and two outputs (maximum ammonia loss, N_max and time to reach half of N_max, K_m). Categorical input variables were transformed into multi-dimensional equal-distance variables, and 13 data were added into 66 training data using a generative adversarial network. Hyperparameters (number of layers, number of neurons, and activation function) of ANN were optimized using Gaussian process. Using 17 test data, the previous ANN model (Lim et al., 2007) showed the mean absolute error (MAE) of K_m and N_max to 0.0668 and 0.1860, respectively. The present ANN outperformed the previous model, reducing MAE by 38% and 56%.

• Journal of Aerospace System Engineering
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• v.16 no.1
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• pp.49-55
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• 2022

The role of drones has been expanded to various fields such as agriculture, construction, and logistics. In particular, agriculture drones are emerging as an effective alternative to solve the problem of labor shortage and reduce the input cost. In this study therefore, we proposed the fruit recognition algorithm and harvesting mechanism for fruit harvesting drone system that can safely harvest fruits at high positions. In the fruit recognition algorithm, we employ "You-Only-Look-Once" which is a deep learning-based object detection algorithm and verify its feasibility by establishing a virtual simulation environment. In addition, we propose the fruit harvesting mechanism which can be operated by a single driving motor. The rotational motion of the motor is converted into a linear motion by the scotch yoke, and the opened gripper moves forward, grips a fruit and rotates it for harvesting. The feasibility of the proposed mechanism is verified by performing Multi-body dynamics analysis.