• Korean Journal of Materials Research
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• v.22 no.11
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• pp.609-614
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• 2012

We report on the NO gas sensing properties of non-directional ZnO nanofibers synthesized using a typical electrospinning technique. These non-directional ZnO nanofibers were electrospun on an $SiO_2$/Si substrate from a solution containing poly vinyl alcohol (PVA) and zinc nitrate hexahydrate dissolved in distilled water. Calcination processing of the ZnO/PVA composite nanofibers resulted in a random network of polycrystalline ZnO nanofibers of 50 nm to 100 nm in diameter. The diameter of the nanofibers was found to depend primarily on the solution viscosity; a proper viscosity was maintained by adding PVA to fabricate uniform ZnO nanofibers. Microstructural measurements using scanning electron microscopy revealed that our synthesized ZnO nanofibers after calcination had coarser surface morphology than those before calcination, indicating that the calcination processing was sufficient to remove organic contents. From the gas sensing response measurements for various NO gas concentrations in dry air at several working temperatures, it was found that gas sensors based on electrospun ZnO nanofibers showed quite good responses, exhibiting a maximum sensitivity to NO gas in dry air at an operating temperature of $200^{\circ}C$. In particular, the non-directional electrospun ZnO nanofiber gas sensors were found to have a good NO gas detection limit of sub-ppm levels in dry air. These results illustrate that non-directional electrospun ZnO nanofibers are promising for use in low-cost, high-performance practical NO gas sensors.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.1
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• pp.1-7
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• 2009

Most developed models are designed to determine pipe diameter, slope and overall layout in order to minimize the cost for the design rainfall for the optimal sewer layout. However, these models are not capable of considering the superposition effect of runoff hydrographs in the sewer pipes. The flow characteristics in the sewer pipes, such as the sewer layout, pipe diameter and slope, vary according to the design of the sewer system. In particular, when the sewer network is modified, the shapes of the runoff hydrographs in the sewer pipes also change because of the superposition effect. In this study, the sewer layout is designed to control and distribute the flows in the sewer pipes, while considering the runoff superposition effect, in order to reduce the inundation risk at each junction. This is accomplished by separating the inflows that enter into each junction by changing the way in which pipes are connected between junctions. And this model combines SWMM (Storm Water Management Model) to perform the hydraulic analysis for the flows in the sewer network. The current sewer layout was modified to minimize the peak outflow at outlet in Garak basin, Seoul, South Korea. As the results, the peak outflows at the outlet were decreased by approximately 20% for the design rainfall during 30 minutes and the total overflows were also decreased for the excessive rainfalls.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• Journal of Practical Agriculture & Fisheries Research
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• v.24 no.4
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• pp.53-61
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• 2022

Growing agricultural products in greenhouses controlled by creating suitable climatic conditions and root zone of crop has been an important research and application subject. Appropriate environmental conditions in greenhouse are necessary for optimum plant growth improved crop yields. This study aimed to establish web-based remote monitoring system which monitors crops growth environment and status of crop on a real-time basis by applying to greenhouses IT technology connecting greenhouse equipment such as temperature sensors, soil sensors, crop sensors and camera. The measuring items were air temperature, relative humidity, solar radiation, CO₂ concentration, EC and pH of nutrient solution, medium temperature, EC of medium, water content of medium, leaf temperature, sap flow, stem diameter, fruit diameter, etc. The developed greenhouse monitoring system was composed of the network system, the data collecting device with sensors, and cameras. Remote monitoring system was implemented in a server/client environment. Information on greenhouse environment and crops is stored in a database. Items on growth and environment is extracted from stored information, could be compared and analyzed. So, A integrated monitoring system for smart greenhouse would be use in application practice and understanding the environment and crop growth for smart greenhouse management. sap flow, stem diameter and pant-water relations

• Economic and Environmental Geology
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• v.46 no.4
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• pp.301-312
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• 2013

Landslides are caused by complex interaction among a large number of interrelated factors such as topography, geology, forest and soils. In this study, a comparative study was carried out using fuzzy relationship method and artificial neural network to evaluate landslide susceptibility. For landslide susceptibility mapping, maps of the landslide occurrence locations, slope angle, aspect, curvature, lithology, soil drainage, soil depth, soil texture, forest type, forest age, forest diameter and forest density were constructed from the spatial data sets. In fuzzy relation analysis, the membership values for each category of thematic layers have been determined using the cosine amplitude method. Then the integration of different thematic layers to produce landslide susceptibility map was performed by Cartesian product operation. In artificial neural network analysis, the relative weight values for causative factors were determined by back propagation algorithm. Landslide susceptibility maps prepared by two approaches were validated by ROC(Receiver Operating Characteristic) curve and AUC(Area Under the Curve). Based on the validation results, both approaches show excellent performance to predict the landslide susceptibility but the performance of the artificial neural network was superior in this study area.

• Advances in concrete construction
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• v.10 no.1
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• pp.81-92
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• 2020

Flexural and splitting strength behavior of conventional concrete can significantly be improved by incorporating the fibers in it. A significant number of research studies have been conducted on various types of fibers and their influence on the tensile capacity of concrete. However, as an important property, tensile capacity of fiber reinforced concrete (FRC) is not modelled properly. Therefore, this paper intends to formulate a model based on experiments that show the relationship between the fiber properties such as the aspect ratio (length/diameter), fiber content, compressive strength, flexural strength and splitting strength of FRC. For the purpose of modeling, various FRC mixes only with steel fiber are adopted from the existing research papers. Automated neural network search (ANS) is then developed and used to investigate the effect of input parameters such as fiber content, aspect ratio and compressive strength to the output parameters of flexural and splitting strength of FRC. It is found that the ANS model can be used to predict the flexural and splitting strength of FRC in a sensible precision.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2A
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• pp.174-180
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• 2004

The World Radiocommunication Conference in year of 2000 adopted new Plans as well as Lists for BSS and its feeder-link in the Regions 1 and 3, based on the new technical criteria such as small size of antenna and low satellite power. Since the new Plans and Lists were based on new technical criteria, ITU was requested to review the relevant regulatory procedures and sharing criteria of broadcasting satellite networks contained in Appendices 30 and 30A of Radio Regulations. Korean BSS network at 116$^{\circ}$E was chosen for the study and ITU S/W (MSPACEG) was used. We analyzed the interference effects from adjacent BSS networks to Korean BSS network using parameters of an antenna diameter and polarization of receiving earth station. The analysis shows that it is difficult to co-operate BSS networks both at 116$^{\circ}$E and 113$^{\circ}$E, however, it is possible to use small antenna (i.e. 45cm) in frequency sharing among BSS networks with 6$^{\circ}$ orbital separation.

• Economic and Environmental Geology
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• v.35 no.1
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• pp.67-74
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• 2002

The purpose of this study is to determine the weights of the factors for landslide susceptibility analysis using artificial neural network. Landslide locations were identified from interpretation of aerial photographs, field survey data, and topography. The landslide-related factors such as topographic slope, topographic curvature, soil drainage, soil effective thickness, soil texture, wood age and wood diameter were extracted from the spatial database in study area, Yongin. Using these factors, the weights of neural networks were calculated by backpropagation training algorithm and were used to determine the weight of landslide factors. Therefore, by interpreting the weights after training, the weight of each landslide factor can be ranked based on its contribution to the classification. The highest weight is topographic slope that is 5.33 and topographic curvature and soil texture are 1 and 1.17, respectively. Weight determination using backprogpagation algorithms can be used for overlay analysis of GIS so the factor that have low weight can be excluded in future analysis to save computation time.

• Journal of Korea Water Resources Association
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• v.36 no.1
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• pp.45-59
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• 2003

The method in this study, which is more efficiency than the existing method, propose the optimal rehabilitation model based on the deterioration prediction of the laying pipe by using the deterioration survey method of the water distribution system. The deterioration prediction model divides the deterioration degree of each pipe into 5 degree by using the probabilistic neural network. Also, the optimal residual durability is estimated by the calculated deterioration degree in each pipe and pipe diameter. The optimal rehabilitation model by integer programming base on the shortest path can calculate a time and cost of maintenance, rehabilitation, and replacement. Also, the model is divided into budget constraint and no budget constraint. Consequently, the model proposed by the study can be utilized as the quantitative method for the management of the water distribution system.

• Structural Engineering and Mechanics
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• v.72 no.3
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• pp.383-393
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• 2019

Horizontal openings in reinforced concrete (RC) beams are quite often used to accommodate service pipelines. Several research papers are available in the literature describing their effect. RC beams with vertical openings are commonly used to accommodate service lines in residential buildings in Kuwait. However, there are lack of design guidelines and best practices reported in the literature for RC beams with vertical openings, whereas the detailed guidelines are available for beams with horizontal openings. In the present paper, laboratory experiments are conducted on nine RC beams with and without vertical openings. Parametric study has been carried out using nonlinear finite element analysis (FEA) with changes in the diameter of the opening, various positions of the opening along the length and width of the beam, edge distance, etc. 50 finite element simulations were conducted. The FEA results are verified using the results from the laboratory experiments. The study showed that the load carrying capacity of the beam is reduced by 20% for the RC beam with vertical openings placed near the center of the beam compared to a solid beam without an opening. Significant reduction in load carrying capacity is observed for beams with an opening near the support (${\approx}15%$). The overall stiffness of the beam, crack pattern and failure modes were not affected due to the presence of the vertical opening. Furthermore, an artificial neural network (ANN) analysis is carried out using the FEA generated data. The results and observations from the ANN and FEA are in good agreement with experimental results.