• Journal of Korea Water Resources Association
• /
• v.37 no.11
• /
• pp.969-978
• /
• 2004

According to the report of hydrologic modeling study, from a quantitative point of view, a lumped model is more efficient than a distributed model. A distributed model has to simplify geospatial characteristics for the shake of restricted application on computer calculation and field observation. In this reason, a distributed model can not help having some errors of water quantity modelling. However, considering a distribution of rainfall-runoff reflected spatial characteristics, a distributed model is more efficient to simulate a flow of surface water, The purpose of this study is modeling of spatial rainfall-runoff of surface water using grid based distributed model, which is consisted of storage function model and essential basin-channel parameters( slope, flow direction & accumulation), and that procedure is able to be executed at a personal computer. The prototype of this model is developed in Heongseong Multipunose Dam basin and adapted in Hapchon Multipurpose Dam basin, which is larger than the former about five times. The efficiency coefficients in result of two dam basin simulations are more than about 0.9, but ones at the upstream water level gauge station meet with bad result owing to overestimated rating curves in high water level. As a result of this study, it is easily implemented that spatially distributed rainfall-runoff model using GIS, and geophysical characteristics of the catchment, hereafter it is anticipated that this model is easily able to apply rainfall data by real time.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.10
• /
• pp.7176-7181
• /
• 2015

This thesis is about the system where the solar module is attached to the high functional guardrail posts with anti-weed, anti-plant, and road-kill applied to produce internal power, enabling the integrated control and real-time monitoring of appearance of wildlife and road conditions using the USN. The whole system consists of a photovoltaic module(PV), a detection sensor(pyroelectric), a controller(operation select and motion sensor), the USN system, the DB(sound and flash), an output unit of sound and flash, and the control system of road-kill prevention and safety induction for vehicles. Thus this study aims to address the remote control and monitoring system of multipurpose guardrails to improve road environment, prevent road-kills, protect wild animals, and guide cars safely by using the USN which is combined with new renewable energy and IT convergence technology. As a result of the study on the remote control and monitoring system using the USN, it was ascertained that the response time of the unmanned sensing system was within 5.1 ms with the current consumption of 0.328 mA, and the data transmission speed of the remote control system was 250 kbps with the current consumption of 0.283 mA.

• Applied Microscopy
• /
• v.34 no.1
• /
• pp.13-21
• /
• 2004

The high voltage electron microscope (HVEM) newly installed in KBSI is an advanced transmission electron microscope capable of atomic resolution (${\leq}1.2{\AA}$ point-to-point resolution) together with high titling function (${\pm}60^{\circ}$), which are suitable to do 3-dimensional atomic imaging of a specimen. In addition, the instrument can be controlled by remote operation system, named as 'FasTEM' for the HVEM, which is favorable to overcome some environmental obstacles resulting from the direct operation. The FasTEM remote operation system has been established between the headquarter of KBSI in Daejeon and the Seoul branch. The server system in the headquarter has been connected with a portable client console system in the Seoul branch using an advanced internet resource, 'KOREN' of 155 Mbps grade. Most of the HVEM functions essential to do remote operation are available on the portable client console. The experiment to acquire the high resolution image of [001] Au has been achieved by excellent transmission of control signals and communication with the HVEM. Real-time reaction like direct operation, such as controls of the illumination and projection parameters, acquisition and adjustment of each detector signal, and electrical steering of each motor-driven system has been realized in remote site. It is positively anticipated that the first remote operation of HVEM in conjunction with IT infraengineering plays a important role in constructing the network based e-Science Grid in Korea for national user s facilities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.824-827
• /
• 2011

Needs for Smart Grid development are increasing all over the world as a solution to its problem according to depletion of energy resources, climatic and environmental rapidly change and growing demand for electrical power. Especially decentralized power is attracting world's attention. In this mood a new era for a unit scale of decentralized power environment is on its way in building. However there is a problem to have to be solved in the uniformity of power quality because the amount of power generated from renewable energy resources such as wind power and solar light is very sensitive to climate fluctuation. And thus this paper tries to suggest an energy management algorithm on basis of real time monitoring for meteorological data. The proposed EMS model embodies the method for predicting the power generation by monitoring and analyzing the climatic data and controling the efficient power distribution between the renewable energy and the existing power. The ultimate goal of this paper is to provide the technological basis for achieving zero-energy house.

• Journal of the Korean Institute of Gas
• /
• v.19 no.2
• /
• pp.74-82
• /
• 2015

Carbon dioxide was designated as one of greenhouse gases that cause global warming. Among various ways to solve the $CO_2$ emission issue, the 3rd-generation biomass (algae) production is considered as a viable method to reduce $CO_2$ in the atmosphere. In this research, we propose a design of an innovative sustainable production system by utilizing the 3rd generation biomass in the environment of floating production storage and offloading (FPSO). Existing biomass production systems depend on the solar energy and they cannot continue producing biomass at night. Electricity produced from offshore wind farms also need an efficient way to store the energy through energy storage system (ESS) or deliver it real-time through power grid, both requiring heavy investment of capital. Thus, we design an offshore grid structure harnessing LED lights to supply the necessary light energy, by using the electricity produced from the wind farm, resulting in the maximized production of biomass and efficient use of wind farm energy. The final design integrates the biomass production system enhanced by LED lights with a wind power generation. The suggested NLP model for the optimal design, implemented in GAMS, would be useful for designing improved offshore biomass production systems combined with the wind farm.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.4
• /
• pp.34-42
• /
• 2017

Since existing nonintrusive appliance load monitoring (NIALM) studies assume that voltage fluctuations are negligible for load identification, and do not affect the identification results, the power factor or harmonic signals associated with voltage are generally not considered parameters for load identification, which limits the application of NIALM in the Smart Home sector. Experiments in this paper indicate that the parameters related to voltage and the characteristics of harmonics should be used to improve the accuracy and reliability of the load monitoring system. Therefore, in this paper, we propose an improved NIALM method that can efficiently analyze the types of household appliances and electrical energy usage in a home network environment. The proposed method is able to analyze the energy usage pattern by analyzing operation characteristics inherent to household appliances using harmonic characteristics of some household appliances as recognition parameters. Through the proposed method, we expect to be able to provide services to the smart grid electric power demand management market and increase the energy efficiency of home appliances actually operating in a home network.

• Journal of Korea Water Resources Association
• /
• v.54 no.7
• /
• pp.453-462
• /
• 2021

As the frequency of localized heavy rainfall has increased during recent years, the importance of high-resolution radar data has also increased. This study aims to correct the bias of Dual Polarization radar that still has a spatial and temporal bias. In many studies, various statistical techniques have been attempted to correct the bias of radar rainfall. In this study, the bias correction of the S-band Dual Polarization radar used in flood forecasting of ME was implemented by a Convolutional Autoencoder (CAE) algorithm, which is a type of Convolutional Neural Network (CNN). The CAE model was trained based on radar data sets that have a 10-min temporal resolution for the July 2017 flood event in Cheongju. The results showed that the newly developed CAE model provided improved simulation results in time and space by reducing the bias of raw radar rainfall. Therefore, the CAE model, which learns the spatial relationship between each adjacent grid, can be used for real-time updates of grid-based climate data generated by radar and satellites.

• Smart Structures and Systems
• /
• v.29 no.1
• /
• pp.1-16
• /
• 2022

Despite recent breakthroughs in deep learning and computer vision fields, the pixel-wise identification of tiny objects in high-resolution images with complex disturbances remains challenging. This study proposes a modified U-net for tiny crack segmentation in real-world steel-box-girder bridges. The modified U-net adopts the common U-net framework and a novel Self-Attention-Self-Adaption (SASA) neuron as the fundamental computing element. The Self-Attention module applies softmax and gate operations to obtain the attention vector. It enables the neuron to focus on the most significant receptive fields when processing large-scale feature maps. The Self-Adaption module consists of a multiplayer perceptron subnet and achieves deeper feature extraction inside a single neuron. For data augmentation, a grid-based crack random elastic deformation (CRED) algorithm is designed to enrich the diversities and irregular shapes of distributed cracks. Grid-based uniform control nodes are first set on both input images and binary labels, random offsets are then employed on these control nodes, and bilinear interpolation is performed for the rest pixels. The proposed SASA neuron and CRED algorithm are simultaneously deployed to train the modified U-net. 200 raw images with a high resolution of 4928 × 3264 are collected, 160 for training and the rest 40 for the test. 512 × 512 patches are generated from the original images by a sliding window with an overlap of 256 as inputs. Results show that the average IoU between the recognized and ground-truth cracks reaches 0.409, which is 29.8% higher than the regular U-net. A five-fold cross-validation study is performed to verify that the proposed method is robust to different training and test images. Ablation experiments further demonstrate the effectiveness of the proposed SASA neuron and CRED algorithm. Promotions of the average IoU individually utilizing the SASA and CRED module add up to the final promotion of the full model, indicating that the SASA and CRED modules contribute to the different stages of model and data in the training process.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.1D
• /
• pp.145-151
• /
• 2009

Recently, the rapid development of GIS technology has made it possible to handle a various data associated with spatially hydrological parameters with their attribute information. Therefore, there has been a shift in focus from lumped runoff models to distributed runoff models, as the latter can consider temporal and spatial variations of discharge. This research is to evaluate the feasibility of GIS based distributed model using radar rainfall which can express temporal and spatial distribution in actual dam watershed during flood runoff period. K-DRUM (K-water hydrologic & hydaulic Distributed flood RUnoff Model) which was developed to calculate flood discharge connected to radar rainfall based on long-term runoff model developed by Kyoto- University DPRI (Disaster Prevention Research Institute), and Yondam-Dam watershed ($930km^2$) was applied as study site. Distributed rainfall according to grid resolution was generated by using preprocess program of radar rainfall, from JIN radar. Also, GIS hydrological parameters were extracted from basic GIS data such as DEM, land cover and soil map, and used as input data of distributed model (K-DRUM). Results of this research can provide a base for building of real-time short-term rainfall runoff forecast system according to flash flood in near future.

• Journal of the Korean GEO-environmental Society
• /
• v.17 no.4
• /
• pp.17-31
• /
• 2016

Many researchers have evaluated the influence of vegetation cover on slope stability. However, due to the extensive variety of site conditions and vegetation types, different studies have often provided inconsistent results, especially when evaluating in different regions. Therefore, additional studies need to be conducted to identify the positive impacts of vegetation cover for slope stabilization. This study used the Transient Rainfall Infiltration and Grid-based Regional Slope-stability Model (TRIGRS) to predict the occurrence of landslides in a watershed in Jinbu-Myeon, Pyeongchang-gun, Korea. The influence of vegetation cover was assessed by spatially and temporally comparing the predicted landslides corresponding to multiple trials of cohesion values (which include the role of root cohesion) and real observed landslide scars to back-calculate the contribution of vegetation cover to slope stabilization. The lower bound of cohesion was defined based on the fact that there are no unstable cells in the raster stability map at initial conditions, and the modified success rate was used to evaluate the model performance. In the next step, the most reliable value representing the contribution of vegetation cover in the study area was applied for landslide assessment. The analyzed results showed that the role of vegetation cover could be replaced by increasing the soil cohesion by 3.8 kPa. Without considering the influence of vegetation cover, a large area of the studied watershed is unconditionally unstable in the initial condition. However, when tree root cohesion is taken into account, the model produces more realistic results with about 76.7% of observed unstable cells and 78.6% of observed stable cells being well predicted.