• Korean Chemical Engineering Research
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• v.60 no.2
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• pp.300-307
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• 2022

Time-resolved serial femtosecond crystallography (TR-SFX) is a powerful technique for determining temporal variations in the structural properties of biomacromolecules on ultra-short time scales without causing structure damage by employing femtosecond X-ray laser pulses generated by an X-ray free electron laser (XFEL). The mixing rate of reactants and biomolecule samples, as well as the hit rate between crystal samples and x-ray pulses, are critical factors determining TR-SFX performance, such as accurate image acquisition and efficient sample consumption. We here develop two distinct sample delivery systems that enable ultra-fast mixing and on-demand droplet injecting via pneumatic application with a square pulse signal. The first strategy relies on inertial mixing, which is caused by the high-speed collision and subsequent coalescence of droplets ejected through a double nozzle, while the second relies on on-demand pneumatic jetting embedded with a 3D-printed micromixer. First, the colliding behaviors of the droplets ejected through the double nozzle, as well as the inertial mixing within the coalesced droplets, are investigated experimentally and numerically. The mixing performance of the pneumatic jetting system with an integrated micromixer is then evaluated by using similar approaches. The sample delivery system devised in this work is very valuable for three-dimensional biomolecular structure analysis, which is critical for elucidating the mechanisms by which certain proteins cause disease, as well as searching for antibody drugs and new drug candidates.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.169-169
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• 2022

Over the past 100 years, the global average temperature has risen by 0.75 ℃. The Korean Peninsula has risen by 1.8 ℃, more than twice the global average. According to the RCP 8.5 scenario, the CO₂ concentration in 2100 will be 940 ppm, about twice as high as current. The National Institute of Crop Science(NICS) is using the SPAR (Soil-Plant Atmosphere Research) facility that can precisely control the environment, such as temperature, humidity, and CO₂. A Python-based colony photosynthesis algorithm has been developed, and the carbon and nitrogen absorption rate of rice is evaluated by setting climate change conditions. In this experiment, Oryza Sativa cv. Shindongjin were planted at the SPAR facility on June 10 and cultivated according to the standard cultivation method. The temperature and CO₂ settings are high temperature and high CO₂ (current temperature+4.7℃ temperature+4.7℃·CO₂ 800ppm), high temperature single condition (current temperature+4.7℃·CO₂ 400ppm) according to the RCP8.5 scenario, Current climate is set as (current temperature·CO₂400ppm). For colony photosynthesis measurement, a LI-820 CO₂ sensor was installed in each chamber for setting the CO₂ concentration and for measuring photosynthesis, respectively. The colony photosynthetic rate in the booting stage was greatest in a high temperature and CO₂ environment, and the higher the nitrogen fertilization level, the higher the colony photosynthetic rate tends to be. The amount of photosynthesis tended to decrease under high temperature. In the high temperature and high CO₂ environment, seed yields, the number of an ear, and 1000 seed weights tended to decrease compared to the current climate. The number of an ear also decreased under the high temperature. But yield tended to increase a little bit under the high temperature and high CO₂ condition than under the high temperature. In addition, In addition to this study, it seems necessary to comprehensively consider the relationship between colony photosynthetic ability, metabolite reaction, and rice yield according to climate change.

• Korean Journal of Applied Biomechanics
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• v.25 no.2
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• pp.157-165
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• 2015

Object : The goals of this research were to make Performance Enhanced Model(PE) taken the largest performance index (PI) through artificial variation of principle components calculated by principle component analysis for trial data, and to verify the effect through comparing kinematic factors between trial data (Raw) and PE. Method : Ten subjects (5 men, 5 women) were recruited and 80% of their maximal record was considered. The PI is a regression equation. In order to develop PE, we extracted Principle components from trial position data (by Principle Components Analysis (PCA)). Before PCA, we made 17 position data to 3 row matrix according to components. We calculated 3 eigen value (principle components) through PCA. And except Y (medial-lateral direction) component (because motion of Y component is small), principle components of X (anterior-posterior direction) and Z (vertical direction) components were changed as following. Changed principle components = principle components + principle components ${\times}$ k. After changing the each principle component, we reconstructed position data using the changed principle components and calculated performance index (PI). A Paired t-test was used to compare Raw data and Performance Enhanced Model data. The level of statistical significance was set at $p{\leq}0.05$. Result : The PI was significantly increased about 12.9kg at PE ($101.92{\pm}6.25$) when compared to the Raw data ($91.29{\pm}7.10$). It means that performance can be increased by optimizing 3D positions. The difference of kinematic factors as follows : the movement distance of the bar from start to lock out was significantly larger (about 1cm) for PE, the width of anterior-posterior bar position in full phase was significantly wider (about 1.3cm) for PE and the horizontal displacement toward the weightlifter after beginning of descent from maximal height was significantly greater (about 0.4cm) for PE. Additionally, the minimum knee angle in the 2-pull phase was significantly smaller (approximately 2.7cm) for the PE compared to that of the Raw. PE was decided at proximal position from the Raw (origin point (0,0)) of PC variation). Conclusion : PI was decided at proximal position from the Raw (origin point (0,0)) of PC variation). This means that Performance Enhanced Model was decided by similar motion to the Raw without a great change. Therefore, weightlifters could be accept Performance Enhanced Model easily, comfortably and without large stress. The Performance Enhance Model can provide training direction for athletes to improve their weightlifting records.

• Journal of the korean academy of Pediatric Dentistry
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• v.41 no.2
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• pp.152-156
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• 2014

The purpose of this study is to compare efficiency of broad spectrum LEDs ($VALO^{(R)}$, Ultradent, USA) with conventional LED curing lights ($Elipar^{TM}$ Freelight 2, 3M ESPE, USA) using a microhardness test. The light curing units used were $VALO^{(R)}$ in three different modes and $Elipar^{TM}$ Freelight 2. The exposure time was used according to the manufacturer's instructions. After cured resin specimens were stored in physiological saline at $37^{\circ}C$ for 24 hours, microhardness was measured using Vickers microhardness tester. The microhardness of upper and lower sides of the specimens were analyzed separately by the ANOVA method (Analysis of Variance) with a significance level set at 5%. At upper side of resin specimens, an increased microhardness was observed in the broad spectrum LED curing light unit with a high power mode for 4 seconds and plasma emulation mode for 20 seconds (p < 0.05). However, at the lower side of resin specimens, there were no significant differences in microhardness between broad spectrum LED curing light unit and conventional LED curing light unit.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.353-358
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• 2003

Recently there has been huge efforts to improve performance in construction process by applying emerging techniques such as the Lean principle, Just-in-time concept and so on. However, little achievement as we expected has come out in reality due to the lack of strategy to set a definite goal of the execution and differences of personal viewpoints on construction productivity. Accordingly, it is the most important to promote the circumstances for the construction process improvement by quantifying the goals of respective unit activity groups. This research explores feasible solutions for the improvement of construction projects performance by combining the six-sigma principle for the generic administrative innovation based on the idea of construction process performance. For this purpose, mutual comparisons of various current approaches are performed in an attempt to establish the advantages in applying six-sigma idea and to provide its fundamental strategy. Furthermore, through a case study with the simulation of applying six-sigma to a unit activity group in construction process, this paper verifies that the overall performance improves as the degree of sigma level gets advanced.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.89-100
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• 2021

The National Cyber Capability Assessment(NCCA) could be used as meaningful information for improving national cyber security policy because it provides information on the elements necessary for strengthening national cyber capabilities and the level of each country. However, there were few studies on improving cyber capabilities using the NCCA result in Korea. Therefore, we analyzed the result of National Cyber Power Index(NCPI) conducted by Belfer Center of Harvard Univ. by applying modified-IPA method to derive cybersecurity policy agendas for Korea. As a result, the need to set agendas on surveillance and offensive cyber capability and improve the effectiveness of policy implementation for intelligence and defense was drawn. Moreover, we suggested need for in-depth study of each policy agenda deduced from preceding research data as a future tasks. And it is expected to increase practical use of NCCA for domestic policy analysis by developing and using our own NCCA model which considered analysis framework proposed in this study.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.121-129
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• 2021

In this study, we try to minimize the tariff risk by constructing a hazardous cargo screening model by applying Association Rule Mining, one of the data mining techniques. For this, the risk level between supply chains is calculated using the Apriori Algorithm, which is an association analysis algorithm, using the big data of the import declaration form of the Korea Customs Service(KCS). We perform data preprocessing and association rule mining to generate a model to be used in screening the supply chain. In the preprocessing process, we extract the attributes required for rule generation from the import declaration data after the error removing process. Then, we generate the rules by using the extracted attributes as inputs to the Apriori algorithm. The generated association rule model is loaded in the KCS screening system. When the import declaration which should be checked is received, the screening system refers to the model and returns the confidence value based on the supply chain information on the import declaration data. The result will be used to determine whether to check the import case. The 5-fold cross-validation of 16.6% precision and 33.8% recall showed that import declaration data for 2 years and 6 months were divided into learning data and test data. This is a result that is about 3.4 times higher in precision and 1.5 times higher in recall than frequency-based methods. This confirms that the proposed method is an effective way to reduce tariff risks.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• Journal of Engineering Education Research
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• v.12 no.1
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• pp.3-16
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• 2009

The purpose of this study is to prepare diverse measures to enhance the utilization of national authorized qualification. The detail objectives of this study are first, to re-establish the utilization scope and range of national authorized qualification through analysis of advance research and theory in various fields of learning on the function of qualification; and second, to set the direction to strengthen the utilization of national authorized qualification. Based on discussions on the various fields of learning on the function of qualification, this study divided the utilization scope of qualification into personal utilization for the benefit of the qualification acquirer him or herself and public utilization for the social and economic benefits. And according to this distinction, we prepared measures to strengthen the utilization of national authorized qualification. First of all, as a way to strengthen personal utilization of the national authorized qualification, we prepared measures to enhance accessibility and facilitate further improvement. As a means to enhance accessibility, we proposed restriction on setting the application condition, diversification of qualification authorization method, facilitation of partial qualification system and minimization of expense required to acquire qualification. Also for the further improvement, we proposed creation of job-level centered grade system and development of job-level centered qualification item by stage. For strengthening the public utilization of national authorized qualification, we come up with ways of strengthening flexibility, transparency, public trust and compatibility. As a way to strengthen flexibility, we proposed establishment of qualification demand monitoring system, expansion of direct participation of users on qualification management, establishment of qualification expiration period and its renewal. For the strengthening of transparency, we proposed to build general qualification information system and to utilize qualification recommendation system. To strengthen public trust, we proposed to strengthen the management of qualification management regulation, secure independency, establish internal audit system and strengthen post management of national authorized qualification. And lastly, we suggested that compatibility comparison standard between qualification and qualification level standard be developed for the strengthening of compatibility.

• Journal of Preventive Medicine and Public Health
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• v.11 no.1
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• pp.123-127
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• 1978

The major objective of this research is to identify those hospital characteristics that best explain cost variation among hospitals and to formulate linear models that can predict hospital costs. Specific emphasis is placed on hospital output, that is, the identification of diagnosis related patient groups (DRGs) which are medically meaningful and demonstrate similar patterns of hospital resource consumption. A casemix index is developed based on the DRGs identified. Considering the common problems encountered in previous hospital cost research, the following study requirements are estab-lished for fulfilling the objectives of this research: 1. Selection of hospitals that exercise similar medical and fiscal practices. 2. Identification of an appropriate data collection mechanism in which demographic and medical characteristics of individual patients as well as accurate and comparable cost information can be derived. 3. Development of a patient classification system in which all the patients treated in hospitals are able to be split into mutually exclusive categories with consistent and stable patterns of resource consumption. 4. Development of a cost finding mechanism through which patient groups' costs can be made comparable across hospitals. A data set of Medicare patients prepared by the Social Security Administration was selected for the study analysis. The data set contained 27,229 record abstracts of Medicare patients discharged from all but one short-term general hospital in Connecticut during the period from January 1, 1971, to December 31, 1972. Each record abstract contained demographic and diagnostic information, as well as charges for specific medical services received. The 'AUT-OGRP System' was used to generate 198 DRGs in which the entire range of Medicare patients were split into mutually exclusive categories, each of which shows a consistent and stable pattern of resource consumption. The 'Departmental Method' was used to generate cost information for the groups of Medicare patients that would be comparable across hospitals. To fulfill the study objectives, an extensive analysis was conducted in the following areas: 1. Analysis of DRGs: in which the level of resource use of each DRG was determined, the length of stay or death rate of each DRG in relation to resource use was characterized, and underlying patterns of the relationships among DRG costs were explained. 2. Exploration of resource use profiles of hospitals; in which the magnitude of differences in the resource uses or death rates incurred in the treatment of Medicare patients among the study hospitals was explored. 3. Casemix analysis; in which four types of casemix-related indices were generated, and the significance of these indices in the explanation of hospital costs was examined. 4. Formulation of linear models to predict hospital costs of Medicare patients; in which nine independent variables (i. e., casemix index, hospital size, complexity of service, teaching activity, location, casemix-adjusted death. rate index, occupancy rate, and casemix-adjusted length of stay index) were used for determining factors in hospital costs. Results from the study analysis indicated that: 1. The system of 198 DRGs for Medicare patient classification was demonstrated not only as a strong tool for determining the pattern of hospital resource utilization of Medicare patients, but also for categorizing patients by their severity of illness. 2. The wei틴fed mean total case cost (TOTC) of the study hospitals for Medicare patients during the study years was $11,27.02 with a standard deviation of $117.20. The hospital with the highest average TOTC ($1538.15) was 2.08 times more expensive than the hospital with the lowest average TOTC ($743.45). The weighted mean per diem total cost (DTOC) of the study hospitals for Medicare patients during the sutdy years was $107.98 with a standard deviation of $15.18. The hospital with the highest average DTOC ($147.23) was 1.87 times more expensive than the hospital with the lowest average DTOC ($78.49). 3. The linear models for each of the six types of hospital costs were formulated using the casemix index and the eight other hospital variables as the determinants. These models explained variance to the extent of 68.7 percent of total case cost (TOTC), 63.5 percent of room and board cost (RMC), 66.2 percent of total ancillary service cost (TANC), 66.3 percent of per diem total cost (DTOC), 56.9 percent of per diem room and board cost (DRMC), and 65.5 percent of per diem ancillary service cost (DTANC). The casemix index alone explained approximately one half of interhospital cost variation: 59.1 percent for TOTC and 44.3 percent for DTOC. Thsee results demonstrate that the casemix index is the most importand determinant of interhospital cost variation Future research and policy implications in regard to the results of this study is envisioned in the following three areas: 1. Utilization of casemix related indices in the Medicare data systems. 2. Refinement of data for hospital cost evaluation. 3. Development of a system for reimbursement and cost control in hospitals.