• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.13 no.6
• /
• pp.590-596
• /
• 2020

In the island areas where system connection with the commercial power grid is difficult, it is quite important to find a method to efficiently manage energy produced with independent microgrids. In this paper, a prosumer management system for P2P power transaction was realized through the testing the power meter and the response rate of the collected data for the power produced in the small-scale microgrids in which hybrid models of solar power and wind power were implemented. The power network of the microgrid prosumer was composed of mesh structure and the P2P power transaction was tested through the power meter and DC power transmitter in the off-grid sites which were independently constructed in three places. The measurement values of the power meter showed significant results of voltage (average): 380V + 0.9V, current (average): + 0.01A, power: 1000W (-1W) with an error range within ±1%. Stabilization of the server was also confirmed with the response rate of 0.32 sec. for the main screen, 2.61 sec. for the cumulative power generation, and 0.11 sec for the power transaction through the transmission of 50 data in real time. Therefore, the proposed system was validated as a P2P power transaction system that can be used as an independent network without transmitted by Korea Electric Power Corporation (KEPCO).

• Clean Technology
• /
• v.27 no.1
• /
• pp.47-54
• /
• 2021

The adsorption of Acid Fuchsin (AF) on granular activated carbon (GAC) was investigated for isothermal adsorption and kinetics and thermodynamic parameters by experimenting with the initial concentration, contact time, temperature, and pH of the dye as adsorption parameters. In the pH effect experiment, the adsorption of AF on activated carbon showed a bathtub type with increased adsorption at pH 3 and 11. The adsorption equilibrium data of AF fit well with the Freundlich isotherm model, and the calculated separation factor (1/n) value was found in which activated carbon can effectively remove AF. The pseudo-second-order kinetic model fits well within 7.88% of the error percent in the adsorption process. According to Weber and Morris's model plot, it was divided into two straight lines. The intraparticle diffusion rate was slow because the stage 2 (intraparticle diffusion) slope was smaller than that of stage 1 (boundary layer diffusion). Therefore, it was confirmed that the intraparticle diffusion was a rate-controlling step. The activation energy of AF (13.00 kJ mol-1) corresponded to the physical adsorption process (5 - 40 kJ mol-1). The free energy change of the AF adsorption by activated carbon showed negative values at 298-318 K. As the spontaneity increased with increasing temperature. The adsorption of AF was an endothermic reaction (ΔH = 22.65 kJ mol-1).

• Clean Technology
• /
• v.27 no.1
• /
• pp.61-68
• /
• 2021

Selective catalytic reduction (SCR) is widely used as a method of removing nitrogen oxide in large-capacity thermal power generation systems. Uniform mixing of the injected ammonia and the inlet flue gas is very important to the performance of the denitrification reduction process in the catalyst bed. In the present study, a computational analysis technique was applied to the ammonia injection system design process of a denitrification facility. The applied model is the denitrification facility of an 800 MW class coal-fired power plant currently in operation. The flow field to be solved ranges from the inlet of the ammonia injection system to the end of the catalyst bed. The flow was analyzed in the two-dimensional domain assuming incompressible. The steady-state turbulent flow was solved with the commercial software named ANSYS-Fluent. The nozzle arrangement gap and injection flow rate in the ammonia injection system were chosen as the design parameters. A total of four (4) cases were simulated and compared. The root mean square of the NH3/NO molar ratio at the inlet of the catalyst layer was chosen as the optimization parameter and the design of the experiment was used as the base of the optimization algorithm. The case where the nozzle pitch and flow rate were adjusted at the same time was the best in terms of flow uniformity.

• Korean Journal of Remote Sensing
• /
• v.36 no.6_2
• /
• pp.1579-1590
• /
• 2020

In this paper, spatially-adaptive denormalization (SPADE) based U-Net is proposed to detect changes by using high-resolution satellite images. The proposed network is to preserve spatial information using SPADE. Change detection methods using high-resolution satellite images can be used to resolve various urban problems such as city planning and forecasting. For using pixel-based change detection, which is a conventional method such as Iteratively Reweighted-Multivariate Alteration Detection (IR-MAD), unchanged areas will be detected as changing areas because changes in pixels are sensitive to the state of the environment such as seasonal changes between images. Therefore, in this paper, to precisely detect the changes of the objects that consist of the city in time-series satellite images, the semantic spatial objects that consist of the city are defined, extracted through deep learning based image segmentation, and then analyzed the changes between areas to carry out change detection. The semantic objects for analyzing changes were defined as six classes: building, road, farmland, vinyl house, forest area, and waterside area. Each network model learned with KOMPSAT-3A satellite images performs a change detection for the time-series KOMPSAT-3 satellite images. For objective assessments for change detection, we use F1-score, kappa. We found that the proposed method gives a better performance compared to U-Net and UNet++ by achieving an average F1-score of 0.77, kappa of 77.29.

• Journal of the Korean Society of Radiology
• /
• v.15 no.4
• /
• pp.547-554
• /
• 2021

In order to quickly and accurately diagnose pneumonia on a chest X-ray image, different batch sizes of 4, 8, 16, and 32 were applied to the same Xception deep learning model, and modeling was performed 3 times, respectively. As a result of the performance evaluation of deep learning modeling, in the case of modeling to which batch size 32 was applied, the results of accuracy, loss function value, mean square error, and learning time per epoch showed the best results. And in the accuracy evaluation of the Test Metric, the modeling applied with batch size 8 showed the best results, and the precision evaluation showed excellent results in all batch sizes. In the recall evaluation, modeling applied with batch size 16 showed the best results, and for F1-score, modeling applied with batch size 16 showed the best results. And the AUC score evaluation was the same for all batch sizes. Based on these results, deep learning modeling with batch size 32 showed high accuracy, stable artificial neural network learning, and excellent speed. It is thought that accurate and rapid lesion detection will be possible if a batch size of 32 is applied in an automatic diagnosis study for feature extraction and classification of pneumonia in chest X-ray images using deep learning in the future.

• Journal of Korea Society of Industrial Information Systems
• /
• v.27 no.2
• /
• pp.71-82
• /
• 2022

In order to replace the sampling tensile test of products produced in the tab welding process, which is one of the automotive battery manufacturing processes, vision inspectors are currently being developed and used. However, the vision inspection has the problem of inspection position error and the cost of improving it. In order to solve these problems, there are recent cases of applying deep learning technology. As one such case, this paper tries to examine the usefulness of applying Faster R-CNN, one of the deep learning technologies, to existing product inspection. The images acquired through the existing vision inspection machine are used as training data and trained using the Faster R-CNN ResNet101 V1 1024x1024 model. The results of the conventional vision test and Faster R-CNN test are compared and analyzed based on the test standards of 0% non-detection and 10% over-detection. The non-detection rate is 34.5% in the conventional vision test and 0% in the Faster R-CNN test. The over-detection rate is 100% in the conventional vision test and 6.9% in Faster R-CNN. From these results, it is confirmed that deep learning technology is very useful for detecting welding error of lead tabs in automobile batteries.

• Journal of the Korea Institute of Building Construction
• /
• v.22 no.6
• /
• pp.681-688
• /
• 2022

In this study, the process of drone photography, automatic volume calculation, total floor area conversion, and waste calculation was constructed as a QGIS plug-in to predict the demolition waste (DW) generated in an aged area where drawing information or building information is uncertain. Through a case study, the high consistency between the automatically calculated volume using the drone and the BIM volume based on the field measurement was confirmed. Field application was carried out for the planned demolition work site, and the consistency between the drone-based volume and the actual measurement-BIM-based volume was reconfirmed. The waste generation unit was applied and the amount of DW was calculated by setting the floor height and building type, and the entire process was completed within 6 hours. Although the difference between building information and building objects through drones occurred according to the setting of temporary structures, loads, and floor heights, it was found that the actual amount of DW was generated more than the initial estimate. It is expected that measures to improve the accuracy of volume and floor area conversion will be required through case studies in the future.

• Geophysics and Geophysical Exploration
• /
• v.24 no.4
• /
• pp.180-193
• /
• 2021

The complex resistivity method is an exploration technique that can obtain various characteristic information of underground media by measuring resistivity and phase in the frequency domain, and its utilization has recently increased. In this paper, a three-dimensional inversion algorithm for the CR data was developed to increase the utilization of this method. The Poisson equation, which can be applied when the electromagnetic coupling effect is ignored, was applied to the modeling, and the inversion algorithm was developed by modifying the existing algorithm by adopting comlex variables. In order to increase the stability of the inversion, a technique was introduced to automatically adjust the Lagrangian multiplier according to the ratio of the error vector and the model update vector. Furthermore, to compensate for the loss of data due to noisy phase data, a two-step inversion method that conducts inversion iterations using only resistivity data in the beginning and both of resistivity and phase data in the second half was developed. As a result of the experiment for the synthetic data, stable inversion results were obtained, and the validity to real data was also confirmed by applying the developed 3D inversion algorithm to the analysis of field data acquired near a hydrothermal mine.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.67 no.4
• /
• pp.274-284
• /
• 2022

To use kenaf (Hibiscus cannabinus L.) as a fiber and livestock feed, a high-yielding variety needs to be identified. For this, accurate phenotyping of plant height is required for this breeding purpose due to the strong relationship between plant height and yield. Plant height can be estimated using RGB images from unmanned aerial vehicles (UAV-RGB) and photogrammetry based on Structure from Motion (SfM) algorithms. In kenaf, accurate measurement of height is limited because kenaf stems have high flexibility and its height is easily affected by wind, growing up to 3 ~ 4 m. Therefore, we aimed to identify a method suitable for the accurate estimation of plant height of kenaf and investigate the feasibility of using the UAV-RGB-derived plant height map. Height estimation derived from UAV-RGB was improved using multi-point calibration against the five different wooden structures with known heights (30, 60, 90, 120, and 150 cm). Using the proposed method, we analyzed the variation in temporal height of 23 kenaf cultivars. Our results demontrated that the actual and estimated heights were reliably comparable with the coefficient of determination (R²) of 0.80 and a slope of 0.94. This method enabled the effective identification of cultivars with significantly different heights at each growth stages.

• Journal of the Korea Organic Resources Recycling Association
• /
• v.31 no.3
• /
• pp.43-53
• /
• 2023

This batch experiment evaluated the impacts of major plant nutrient releases by applying the modified Hyperbola model on the leachates and N₂O emissions from incorporated rice hull biochar with organic fertilizer materials. The treatments consisted of the control as incorporated with organic fertilizer materials, the incorporated rice hull biochar with organic fertilizer materials, and the incorporated plasma-activated rice hull biochar with organic fertilizer materials under redox conditions. The results indicated that the maximum release amount of NH₄-N was 3486.3 mg L^-1 in the control, and their reduction rates of NH₄-N, NO₃-N, PO₄-P, and K were 8.0%, 17.5% 44.3.0% and 8.7%, respectively, relative to the control. In the control, the highest soluble amount of PO₄-P was 681.0 mg L^-1. The estimations for accumulated NH₄-N, NO₃-N, PO₄-P, and K-releases in all the treatments were significantly (p<0.01) fitted with a modified Hyperbola model. For greenhouse gas emissions, the lowest cumulative N₂O was 340.4 mg kg^-1 in the soil incorporated with plasma-activated rice hull biochar, and the reduction rates were 27.8% and 86.4% in the rice hull biochar and plasma-activated rice hull biochar treatments, respectively, compared to the control. Therefore, it concluded that the incorporated rice hull biochar can be especially useful for controlling PO₄-P release and N₂O emissions for bio-fertilizer applications.