• 한국대기환경학회지
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• 제29권4호
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• pp.407-438
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• 2013

Current status and future direction of air quality modeling for monitoring and forecasting air quality in East Asia were discussed in this paper. An integrated air quality modeling system, combining (1) emission processing and modeling, (2) meteorological model simulation, (3) chemistry-transport model (CTM) simulation, (4) ground-based and satellite-retrieved observations, and (5) data assimilation, was introduced. Also, the strategies for future development of the integrated air quality modeling system in East Asia was discussed in this paper. In particular, it was emphasized that the successful use and development of the air quality modeling system should depend on the active applications of the data sets from incumbent and upcoming LEO/GEO (Low Earth Orbit/Geostationary Earth Orbit) satellites. This is particularly true, since Korea government successfully launched Geostationary Ocean Color Imager (GOCI) in June, 2010 and has another plan to launch Geostationary Environmental Monitoring Spectrometer (GEMS) in 2018, in order to monitor the air quality and emissions in/around the Korean peninsula as well as over East Asia.

• Journal of Korean Neurosurgical Society
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• 제62권1호
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• pp.53-60
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• 2019

Objective : The purpose of this study was to determine the efficacy of intra-operative cell salvage system (ICS) to decrease the need for allogeneic transfusions in patients undergoing major spinal deformity surgeries. Methods : A total of 113 consecutive patients undergoing long level posterior spinal segmental instrumented fusion (${\geq}5$ levels) for spinal deformity correction were enrolled. Data including the osteotomy status, the number of fused segments, estimated blood loss, intra-operative transfusion amount by ICS (Cell $Saver^{(R)}$, $Haemonetics^{(C)}$, Baltimore, MA, USA) or allogeneic blood, postoperative transfusion amount, and operative time were collected and analyzed. Results : The number of patients was 81 in ICS group and 32 in non-ICS group. There were no significant differences in demographic data and comorbidities between the groups. Autotransfusion by ICS system was performed in 53 patients out of 81 in the ICS group (65.4%) and the amount of transfused blood by ICS was 226.7 mL in ICS group. The mean intra-operative allogeneic blood transfusion requirement was significantly lower in the ICS group than non-ICS group (2.0 vs. 2.9 units, p=0.033). The regression coefficient of ICS use was -1.036. Conclusion : ICS use could decrease the need for intra-operative allogeneic blood transfusion. Specifically, the use of ICS may reduce about one unit amount of allogeneic transfusion in major spinal deformity surgery.

• 대한원격탐사학회지
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• 제26권3호
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• pp.317-324
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• 2010

In recent years, many automatic classification approaches have been employed. An automatic classification method can be effective, time-saving and can produce objective results due to the exclusion of operator intervention. This paper proposes a classification method based on automated training for high resolution multispectral images using ancillary data. Generally, it is problematic to automatically classify high resolution images using ancillary data, because of the scale difference between the high resolution image and the ancillary data. In order to overcome this problem, the proposed method utilizes the classification results of a Landsat image as a medium for automatic classification. For the classification of a Landsat image, a maximum likelihood classification is applied to the image, and the attributes of ancillary data are entered as the training data. In the case of a high resolution image, a K-means clustering algorithm, an unsupervised classification, was conducted and the result was compared to the classification results of the Landsat image. Subsequently, the training data of the high resolution image was automatically extracted using regular rules based on a RELATIONAL matrix that shows the relation between the two results. Finally, a high resolution image was classified and updated using the extracted training data. The proposed method was applied to QuickBird and SPOT-5 images of non-accessible areas. The result showed good performance in accuracy assessments. Therefore, we expect that the method can be effectively used to automatically construct thematic maps for non-accessible areas and update areas that do not have any attributes in geographic information system.

• 산업융합연구
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• 제20권12호
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• pp.11-25
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• 2022

본 연구에서는 블록체인이 적용된 공급망관리 정보시스템으로 전환의도에 영향을 미치는 요인을 검증하고자한다. 이를 위해 선행연구의 고찰을 통해 변수 선정 및 연구모형을 구성하고, TOE 프레임워크와 PPM 모델을 활용해 실증분석을 시행하였다. Push 요인, Pull 요인이 블록체인 시스템 전환의도에 미치는 영향 및 Mooring 요인인 전환비용을 통한 조절효과를 검증하였다. 국내에 소재한 중소기업을 대상으로 설문을 하여 320개 응답 자료를 표본으로 구조방정식 모형을 사용해 가설을 검증하였다. 연구 결과 Push 요인인 사회적 영향과 Pull 요인인 경영진의 혁신의지가 전환의도에 유의미한 영향을 미쳤다. 그리고 전환비용 인식 수준이 높고 낮은 집단 간 조절효과를 확인하였다. 본 연구는 블록체인 기반 공급망관리 정보시스템의 구현을 통한 기업의 경쟁력을 제고시킬 수 있는 SCBM(supply chain & blockchain management)의 개념 및 연구 방향을 제시하였다는 점에 의의가 있다.

• 한국수소및신에너지학회논문집
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• 제24권4호
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• pp.295-301
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• 2013

Long-term global energy-demand growth is expected to increase driven by strong energy-demand growth from developing countries. Fusion power offers the prospect of an almost inexhaustible source of energy for future generations, even though it also presents so far insurmountable scientific and engineering challenges. One of the challenges is safe handling of hydrogen isotopes. Metal hydrides such as depleted uranium hydride or ZrCo hydride are used as a storage medium for hydrogen isotopes reversibly. The metal hydrides bind with hydrogen very strongly. In this paper, we carried out a modeling and simulation work for absorption/desorption of hydrogen by ZrCo in a horizontal annulus cylinder bed. A comprehensive mathematical description of a metal hydride hydrogen storage vessel was developed. This model was calibrated against experimental data obtained from our experimental system containing ZrCo metal hydride. The model was capable of predicting the performance of the bed for not only both the storage and delivery processes but also heat transfer operations. This model should thus be very useful for the design and development of the next generation of metal hydride hydrogen isotope storage systems.

• BMB Reports
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• 제40권6호
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• pp.936-943
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• 2007

Previously it was reported that promoter of groES-groEL operon of Staphylococcus aureus is induced by various cellwall active antibiotics. In order to exploit the above promoter for identifying novel antistaphylococcal drugs, we have cloned the promoter containing region ($P_g$) of groES-groEL operon of S. aureus Newman and found that the above promoter is induced by sublethal concentrations of many antibiotics including cell-wall active antibiotics. A reporter S. aureus RN4220 strain (designated SAU006) was constructed by inserting the $P_g$-lacZ transcriptional fusion into its chromosome. Agarose-based assay developed with SAU006 shows that $P_g$ in single-copy is also induced distinctly by different classes of antibiotics. Data indicate that ciprofloxacin, rifampicin, ampicillin, and cephalothin are strong inducers, whereas, tetracycline, streptomycin and vancomycin induce the above promoter weakly. Sublethal concentrations of ciprofloxacin and ampicilin even have induced $P_g$ efficiently in microtiter plate grown SAU006. Additional studies show for the first time that above promoter is also induced weakly by arsenate salt and hydrogen peroxide. Taken together, we suggest that our simple and sensitive assay systems with SAU006 could be utilized for screening and detecting not only novel antistaphylococcal compounds but also different toxic chemicals.

• 산업경영시스템학회지
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• 제38권3호
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• pp.29-38
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• 2015

In this paper, scale efficiencies and relative efficiencies of R&D projects in Industrial Technology Program, sponsored by Ministry of Trade, Industry and Energy, Korea, are calculated and compared. For the process, various DEA (Data Envelopment Analysis) models are adopted as major techniques. For DEA, two stage input oriented models are utilized for calculating the efficiencies. Next, the calculated efficiencies are grouped according to their subprograms (Industrial Material, IT Fusion, Nano Fusion, Energy Resources, and Resources Technology) and recipient types (Public Enterprise, Large Enterprise, Medium Enterprise, Small Enterprise, Lab., Univ., and etc.) respectively. Then various subprograms and recipient types are compared in terms of scale efficiencies (CCR models) and relative efficiencies (BCC models). In addition, the correlation between the 1st stage relative efficiencies and the 2nd stage relative efficiencies is calculated, from which the causal relationship between them can be inferred. Statistical analysis shows that the amount of input, in general, should increase in order to be scale efficient (CCR models) regardless of the subprograms and recipient types, that the 1st and 2nd stage relative efficiencies are different in terms of the programs and recipient types (BCC models), and that there is no significant correlation between the 1st stage relative efficiencies and the 2nd stage relative efficiencies. However, the results should be used only as reference because the goal each and every subprogram has is different and the situation each and every recipient type faces is different. In addition, the causal link between the 1st stage relative efficiencies and the 2nd relative efficiencies is not considered, which, in turn, is the limitation of this paper.

• 대한임베디드공학회논문지
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• 제18권6호
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• pp.267-275
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• 2023

This paper presents the development of specialized software for annotating volume-of-interest on ¹⁸F-FDG PET/CT images with the goal of facilitating the studies and diagnosis of head and neck cancer (HNC). To achieve an efficient annotation process, we employed the SE-Norm-Residual Layer-based U-Net model. This model exhibited outstanding proficiency to segment cancerous regions within ¹⁸F-FDG PET/CT scans of HNC cases. Manual annotation function was also integrated, allowing researchers and clinicians to validate and refine annotations based on dataset characteristics. Workspace has a display with fusion of both PET and CT images, providing enhance user convenience through simultaneous visualization. The performance of deeplearning model was validated using a Hecktor 2021 dataset, and subsequently developed semi-automatic annotation functionalities. We began by performing image preprocessing including resampling, normalization, and co-registration, followed by an evaluation of the deep learning model performance. This model was integrated into the software, serving as an initial automatic segmentation step. Users can manually refine pre-segmented regions to correct false positives and false negatives. Annotation images are subsequently saved along with their corresponding ¹⁸F-FDG PET/CT fusion images, enabling their application across various domains. In this study, we developed a semi-automatic annotation software designed for efficiently generating annotated lesion images, with applications in HNC research and diagnosis. The findings indicated that this software surpasses conventional tools, particularly in the context of HNC-specific annotation with ¹⁸F-FDG PET/CT data. Consequently, developed software offers a robust solution for producing annotated datasets, driving advances in the studies and diagnosis of HNC.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권4호
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• pp.217-231
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• 2015

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of Multiple Instance Learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Mamdani Fuzzy Inference Systems (MI-Mamdani). In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. Fuzzy logic is powerful at modeling knowledge uncertainty and measurements imprecision. It is one of the best frameworks to model vagueness. However, in addition to uncertainty and imprecision, there is a third vagueness concept that fuzzy logic does not address quiet well, yet. This vagueness concept is due to the ambiguity that arises when the data have multiple forms of expression, this is the case for multiple instance problems. In this paper, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, a MI-Mamdani that extends the standard Mamdani inference system to compute with multiple instances is introduced. The proposed framework is tested and validated using a synthetic dataset suitable for MIL problems. Additionally, we apply the proposed multiple instance inference to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.

• 한국측량학회지
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• 제35권5호
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• pp.339-348
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• 2017

Due to recent improvements in computer processing speed and image processing technology, researches are being actively carried out to combine information from a camera with existing GNSS (Global Navigation Satellite System) and dead reckoning. In this study, the mathematical model based on SPR (Single Photo Resection) is derived for image-based assistant algorithm for vehicle positioning. Simulation test is performed to analyze factors affecting SPR. In addition, GNSS/on-board vehicle sensor/image based positioning algorithm is developed by combining image-based positioning algorithm with existing positioning algorithm. The performance of the integrated algorithm is evaluated by the actual driving test and landmark's position data, which is required to perform SPR, based on simulation. The precision of the horizontal position error is 1.79m in the case of the existing positioning algorithm, and that of the integrated positioning algorithm is 0.12m at the points where SPR is performed. In future research, it is necessary to develop an optimized algorithm based on the actual landmark's position data.