• Korean Journal of Optics and Photonics
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• v.14 no.3
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• pp.331-337
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• 2003

This paper represents 50 ㎓ narrow band pass filters for fiber optical communication fabricated by dual ion beam sputtering method. We have analyzed the characteristics of the TA$_2$ $O_{5}$ and $SiO_2$ single layers in order to optimize the process conditions for the 50 ㎓ narrow band pass filters, and controlled the film thickness uniformity to less than 0.1 nm deviation by dual peak spike filter pre-deposition. We designed and fabricated 50 ㎓ narrow band pass filters that consist of 216 layers including 4 cavities based on quarter wave optical thickness. Class substrates with high thermal expansion coefficients were used to reduce the film stress. Anti-reflection coating at the rear side of the substrate was also needed to reduce the optical thickness errors of the Optical Monitoring System caused by multiple beam interference between the front side and the rear side of substrate. The optical characteristics of this 50 ㎓ narrow band pass filters are insertion loss of 0.40 ㏈, pass band ripple of 0.20 ㏈, and pass bandwidth at -0.5 ㏈ of 0.20 nm. and isolation bandwidth at -25 ㏈ of 0.6 nm, which satisfy specifications of dense WDM system in fiber optical communications.tions.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.4
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• pp.223-233
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• 2021

Systematic and continuous monitoring and management of the energy consumption of buildings are important for estimating building energy efficiency, and ultimately aim to cope with climate change and establish effective policies for environment, and energy supply and demand policies. Globally, buildings consume 36% of total energy and account for 39% of carbon dioxide emissions. The purpose of this study is to generate three-dimensional thermo-spatial building models with photogrammetric technique using drone TIR (Thermal Infrared) images to measure the temperature emitted from a building, that is essential for the building energy rating system. The aerial triangulation was performed with both optical and TIR images taken from the sensor mounted on the drone, and the accuracy of the models was analyzed. In addition, the thermo-spatial models of temperature distribution of the buildings in three-dimension were visualized. Although shape of the objects 3D building modeling is relatively inaccurate as the spatial and radiometric resolution of the TIR images are lower than that of optical images, TIR imagery could be used effectively to measure the thermal energy of the buildings based on spatial information. This paper could be meaningful to present extension of photogrammetry to various application. The energy consumption could be quantitatively estimated using the temperature emitted from the individual buildings that eventually would be uses as essential information for building energy efficiency rating system.

• Journal of Chest Surgery
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• v.54 no.5
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• pp.377-382
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• 2021

Background: Subclavian vein (SV) catheterization is a method for the delivery of fluids, drugs, and blood products, venous blood sampling, and central vein pressure monitoring in cardiac surgery. Catheter occlusion is a serious complication of SV catheterization during cardiac surgery, especially after sternal retractor expansion. Methods: In this observational study, 303 patients who had successful right infraclavicular SV catheterization from September 2019 to April 2020 were enrolled to determine the incidence of catheter occlusion. After catheterization, the lumens of all catheters were checked for the ability to infuse and withdraw blood from the catheter before and after sternal retractor expansion. The patients' characteristics, cannulation approach, on-pump or off-pump technique, occlusion of the catheter and its lumens, and any associated complications were recorded. The data were analyzed using IBM SPSS ver. 22.0 (IBM Corp., Armonk, NY, USA). Results: Of the 303 patients studied, 205 were male (67.7%) and 98 were female (32.3%). Catheter occlusion occurred in 11 patients with on-pump cardiopulmonary bypass (CPB) (227 patients) and 4 patients with off-pump CPB (76 patients) (p=0.863). The incidence of catheter occlusion was 4.95% (15 of 303 patients) with no cases of simultaneous 3-lumen occlusion in a catheter. The most commonly occluded lumen was the distal lumen (57.92%). Simultaneous 2-lumen occlusion occurred in 4 patients. Catheter occlusion was found in 3 of 13 malpositioned catheters (23.07%). Conclusion: The current study showed that malpositioning of the catheter tip was a risk factor for catheter occlusion and that the distal lumen of a triple-lumen catheter was the most commonly occluded lumen.

• Journal of Wetlands Research
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• v.21 no.3
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• pp.191-198
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• 2019

High impervious surfaces increase the surface runoff during rainfall and reduces the underground infiltration thereby leading to water cycle distortion. The distortion of water cycle causes various urban environmental problems such as urban flooding, drought, water pollutant due to non-point pollution runoff, and water ecosystem damage. Climate change intensified seasonal biases in urban rainfall and affected urban microclimate, thereby increasing the intensity and frequency of urban floods and droughts. Low impact development(LID) technology has been applied to various purposes as a technique to reduce urban environmental problems caused by water by restoring the natural water cycle in the city. This study evaluated the contribution of hydrologic characteristics and water cycle recovery after LID application using long-term monitoring results of various LID technology applied in urban areas. Based on the results, the high retention and infiltration rate of the LID facility was found to contribute significantly to peak flow reduction and runoff delay during rainfall. The average runoff reduction effect was more than 60% at the LID facility. The surface area of the LID facility area ratio(SA/CA) was evaluated as an important factor affecting peak flow reduction and runoff delay effect.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.209-221
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• 2015

Temperature is one of the most important factors affecting crop growth. The diurnal cycle of the scale factor [Tsc] for air temperature and the surface temperature of crop leaf and soil could be estimated by the following equation : $[Tsc]=0.5{\times}sin(X+C)+0.5$. The daily air temperature (E[Ti]) according to the E&E time [X] can be estimated by following equation using average (Tavg), maximum (Tm) and minimum (Tn) temperature : $E[Ti]=Tn+(Tm-Tn){\times}[0.5{\times}sin\;\{X+(9.646Tavg+703.65)\}+0.5]$. The crop leaf temperature in 24th June 2014 was high as the order of red pepper without mulching > red pepper with mulching > soybean under drought > soybean with irrigation > Chinese cabbage. The case in estimating crop leaf surface temperature using air temperature and soil surface temperature was lower in the deviation compared to the case using air temperature for Chinese cabbage and red pepper. These results can be utilized for the crop models as input data with estimation.

• Korean Journal of Remote Sensing
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• v.34 no.6_4
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• pp.1555-1565
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• 2018

Detection of subtle ground deformation of volcanoes plays an important role in evaluating the risk and possibility of volcanic eruptions. Ground-fixed observation equipment is difficult to maintain and cost-inefficient. In contrast, satellite remote sensing can regularly monitor at low cost. In this paper, following the study of Chadwick et al. (2006), which applied the interferometric SAR (InSAR) technique to the Sierra Negra volcano, Galapagos. In order to investigate the deformation of the volcano before 2005 eruption, the recent activities of this volcano were analyzed using Sentinel-1, the latest SAR satellite. We obtained the descending mode Sentinel-1A SAR data from January 2017 to January 2018, applied the Persistent Scatter InSAR, and estimated the depth and expansion quantity of magma in recent years through the Mogi model. As a result, it was confirmed that the activity pattern of volcano prior to the eruption in June 2018 was similar to the pattern before the eruption in 2005 and was successful in estimating the depth and expansion amount. The results of this study suggest that satellite SAR can characterize the activity patterns of volcano and can be possibly used for early monitoring of volcanic eruption.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.27-35
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• 2019

The purpose of this study is to analyze the displacement characteristics in slow-moving landslide area using digital elevation model and airborne LiDAR when unpredictable disaster such as slow-moving landslide occurred. We also aimed to provide basic data for establishing a rapid, reasonable and effective restoration plan. In this study, slow-moving landslide occurrence cracks were selected through the airborne LiDAR data, and the topographic changes and the scale of occurrence were quantitatively analyzed. As a result of the analysis, the study area showed horseshoe shape similar to the general form of slow-moving landslide occurrence in Korea, and the direction of movement was in the north direction. The total area of slow-moving landslide damage was estimated to about 2.5ha, length of landsldie scrap 327.3m, average width 19.3m, and average depth 8.6m. The slow-moving landslides did not occur on a large scale but occurred on the adjacent slope where roads were located, caused damage to retaining walls and roads. The field survey of slow-moving landslides was limited by accessibility and safety issues, but there was an advantage that accurate analysis was possible through the airborne LiDAR. However, because airborne LiDAR has costly disadvantages, it has proposed a technique to mount LiDAR on UAV for rapidity, long-term monitoring. In a slow-moving landslide damage area, information such as direction of movement of cracks and change of scale should be acquired continuously to be used in restoration planning and prevention of damage.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.161-168
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• 2019

Recently, video equipments such as CCTV, which is spreading rapidly, is being used as a means to monitor and cope with abnormal situations in almost governments, companies, and households. However, in most cases, since recognizing the abnormal situation is carried out by the monitoring person, the immediate response is difficult and is used only for post-analysis. In this paper, we present the results of the development of video surveillance system that automatically recognizing the abnormal situations and sending such events to the smartphone immediately using the latest deep learning technology. The proposed system extracts skeletons from the human objects in real time using Openpose library and then recognizes the human behaviors automatically using deep learning technology. To this end, we reconstruct Openpose library, which developed in the Caffe framework, on Darknet framework to improve real-time processing. We also verified the performance improvement through experiments. The system to be introduced in this paper has accurate and fast behavioral recognition performance and scalability, so it is expected that it can be used for video surveillance systems for various applications.

• Journal of the Korean Society of International Agriculture
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• v.30 no.4
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• pp.339-346
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• 2018

Cyanazine is a member of the triazine family of herbicides. Cyanazine is used as a pre- and post-emergence herbicide for the control of annual grasses and broadleaf weeds. This experiment was conducted to establish a determination method for cyanazine, as domestic unregistered pesticide, residue in major agricultural commodities using HPLC-DAD/MS. Cyanazine was extracted with acetone from representative samples of five raw products which comprised apple, green pepper, Kimchi cabbage, hulled rice and soybean. The extract was diluted with saline water and partitioned to dichloromethane for remove polar extractive in the aqueous phase. For the hulled rice and soybean samples, n-hexane/acetonitrile partition was additionally employed to remove non-polar lipids. The extract was finally purified by optimized florisil column chromatography. On a $C_{18}$ column in HPLC, cyanazine was successfully separated from co-extractives of sample, and sensitively quantitated by diode array detection at 220 nm. Accuracy and precision of the proposed method was validated by the recovery experiment on every major agricultural commodity samples fortified with cyanazine at 3 concentration levels per agricultural commodity in each triplication. Mean recoveries were ranged from 83.6 to 93.3% in five major representative agricultural commodities. The coefficients of variation were all less than 10%, irrespective of sample types and fortification levels. Limit of quantitation(LOQ) of cyanazine was 0.02 mg/kg as verified by the recovery experiment. A confirmatory method using LC/MS with selected-ion monitoring(SIM) technique was also provided to clearly identify the suspected residue.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.93-103
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• 2021

Despite the efforts of financial authorities in conducting the direct management and supervision of collection agents and bond-collecting guideline, the illegal and unfair collection of debts still exist. To effectively prevent such illegal and unfair debt collection activities, we need a method for strengthening the monitoring of illegal collection activities even with little manpower using technologies such as unstructured data machine learning. In this study, we propose a classification model for illegal debt collection that combine machine learning such as Support Vector Machine (SVM) with a rule-based technique that obtains the collection transcript of loan companies and converts them into text data to identify illegal activities. Moreover, the study also compares how accurate identification was made in accordance with the machine learning algorithm. The study shows that a case of using the combination of the rule-based illegal rules and machine learning for classification has higher accuracy than the classification model of the previous study that applied only machine learning. This study is the first attempt to classify illegalities by combining rule-based illegal detection rules with machine learning. If further research will be conducted to improve the model's completeness, it will greatly contribute in preventing consumer damage from illegal debt collection activities.