• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.281-289
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• 2022

Plasma ozone is utilized in a variety of applications in the field of sterilization due to its high sterilization performance. Dielectric materials used in DBD(dielectric barrier discharges) are mainly polymer, quartz and ceramics. These dielectric layers have the advantage of limiting the amount of supplied electron charge and allowing plasma to occur evenly on the surface of dielectric. Actually, the target or environment for sterilization is often a complex structure, so research and academic study are needed by utilizing the concept of space sterilization. In this study, the device is applied to generate DBD plasma at atmospheric pressure for disinfection due to the effectiveness in producing radicals and ozone. The generator of plasma ozone is a basic structure of dielectric barrier discharge by placing ceramic tube dielectrics and stainless steel electrical conductors at regular intervals. Various applications can be developed based on the proposed design method. Plasma ozone generation for space sterilization device is recognized as an excellent sterilization device. Through the design and verification of the device, we intend to establish an optimal design of the spatial sterilization device and provide the basis data for sterilization applications.

• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.545-556
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• 2022

It is recommended to use long-term hydrometeorological data for more than the service life of the hydraulic structures and water resource planning. For the purpose of expanding rainfall data, stochastic simulation models, such as Modified Bartlett-Lewis Rectangular Pulse (BLRP) and Neyman-Scott Rectangular Pulse (NSRP) models, have been widely used. The optimal parameters of the model can be estimated by repeatedly comparing the statistical moments defined through a combination of parameters of the probability distribution in the optimization context. However, parameter estimation using relatively small observed rainfall statistics corresponds to an ill-posed problem, leading to an increase in uncertainty in the parameter estimation process. In addition, as shown in previous studies, extreme values are underestimated because objective functions are typically defined by the first and second statistical moments (i.e., mean and variance). In this regard, this study estimated the parameters of the NSRP model using the objective function with the third moment and compared it with the existing approach based on the first and second moments in terms of estimation of extreme rainfall. It was found that the first and second moments did not show a significant difference depending on whether or not the skewness was considered in the objective function. However, the proposed model showed significantly improved performance in terms of estimation of design rainfalls.

• Journal of the Korean Society of Radiology
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• v.16 no.3
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• pp.241-248
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• 2022

A guideline for determining the optimal catheter length according to the patient's physique during tunnel-type dialysis catheter insertion used in renal failure patients is presented, and the maintenance of the function of the dialysis catheter is evaluated. From October 1, 2015 to February 31, 2016, a total of 110 (male : female = 73 : 37) patients who underwent tunnel-type dialysis catheterization performed at our hospital were treated without using guidelines. The group was divided into A group, and the group treated using the guideline into B group. Results were analyzed statistically using SPSS 20.0. As a result of the dialysis catheter insertion procedure according to the use of guidelines, the function maintenance rate of group A was 81.8%, and the function maintenance rate of group B was 96.3%. In the case of tunnel dialysis catheter procedure, the function maintenance effect was statistically significant in the group treated using the guideline compared to the group not using it (p<0.05). It can be said that it is more effective when information on the patient's underlying disease is reflected in addition to the use of guidelines.

• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.341-352
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• 2022

Medical image segmentation is the most important task in radiation therapy. Especially, when segmenting medical images, the liver is one of the most difficult organs to segment because it has various shapes and is close to other organs. Therefore, automatic segmentation of the liver in computed tomography (CT) images is a difficult task. Since tumors also have low contrast in surrounding tissues, and the shape, location, size, and number of tumors vary from patient to patient, accurate tumor segmentation takes a long time. In this study, we propose a method algorithm for automatically segmenting the liver and tumor for this purpose. As an advantage of setting the boundaries of the tumor, the liver and tumor were automatically segmented from the CT image using the 2D CoordConv DeepLab V3+ model using the CoordConv layer. For tumors, only cropped liver images were used to improve accuracy. Additionally, to increase the segmentation accuracy, augmentation, preprocess, loss function, and hyperparameter were used to find optimal values. We compared the CoordConv DeepLab v3+ model using the CoordConv layer and the DeepLab V3+ model without the CoordConv layer to determine whether they affected the segmentation accuracy. The data sets used included 131 hepatic tumor segmentation (LiTS) challenge data sets (100 train sets, 16 validation sets, and 15 test sets). Additional learned data were tested using 15 clinical data from Seoul St. Mary's Hospital. The evaluation was compared with the study results learned with a two-dimensional deep learning-based model. Dice values without the CoordConv layer achieved 0.965 ± 0.01 for liver segmentation and 0.925 ± 0.04 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.927 ± 0.02 for liver division and 0.903 ± 0.05 for tumor division. The dice values using the CoordConv layer achieved 0.989 ± 0.02 for liver segmentation and 0.937 ± 0.07 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.944 ± 0.02 for liver division and 0.916 ± 0.18 for tumor division. The use of CoordConv layers improves the segmentation accuracy. The highest of the most recently published values were 0.960 and 0.749 for liver and tumor division, respectively. However, better performance was achieved with 0.989 and 0.937 results for liver and tumor, which would have been used with the algorithm proposed in this study. The algorithm proposed in this study can play a useful role in treatment planning by improving contouring accuracy and reducing time when segmentation evaluation of liver and tumor is performed. And accurate identification of liver anatomy in medical imaging applications, such as surgical planning, as well as radiotherapy, which can leverage the findings of this study, can help clinical evaluation of the risks and benefits of liver intervention.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1183-1193
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• 2021

Low flow affects various fields such as river water supply management and planning, and irrigation water. A sufficient period of flow data is required to calculate the Flow Duration Curve. However, in order to calculate the Flow Duration Curve, it is essential to secure flow data for more than 30 years. However, in the case of rivers below the national river unit, there is no long-term flow data or there are observed data missing for a certain period in the middle, so there is a limit to calculating the Flow Duration Curve for each river. In the past, statistical-based methods such as Multiple Regression Analysis and ARIMA models were used to predict sulfur in the unmeasured watershed, but recently, the demand for machine learning and deep learning models is increasing. Therefore, in this study, we present the DNN technique, which is a machine learning technique that fits the latest paradigm. The DNN technique is a method that compensates for the shortcomings of the ANN technique, such as difficult to find optimal parameter values in the learning process and slow learning time. Therefore, in this study, the Flow Duration Curve applicable to the unmeasured watershed is calculated using the DNN model. First, the factors affecting the Flow Duration Curve were collected and statistically significant variables were selected through multicollinearity analysis between the factors, and input data were built into the machine learning model. The effectiveness of machine learning techniques was reviewed through statistical verification.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1037-1051
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• 2021

Biochemical oxygen demand (BOD) concentration, one of important water quality indicators, is treated as the measuring item for the ecological chapter in lakes and rivers. This investigation employed novel two-stage hybrid paradigm (i.e., wavelet-based gated recurrent unit, wavelet-based generalized regression neural networks, and wavelet-based random forests) to predict BOD concentration in the Dosan and Hwangji stations, South Korea. These models were assessed with the corresponding independent models (i.e., gated recurrent unit, generalized regression neural networks, and random forests). Diverse water quality and quantity indicators were implemented for developing independent and two-stage hybrid models based on several input combinations (i.e., Divisions 1-5). The addressed models were evaluated using three statistical indices including the root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and correlation coefficient (CC). It can be found from results that the two-stage hybrid models cannot always enhance the predictive precision of independent models confidently. Results showed that the DWT-RF5 (RMSE = 0.108 mg/L) model provided more accurate prediction of BOD concentration compared to other optimal models in Dosan station, and the DWT-GRNN4 (RMSE = 0.132 mg/L) model was the best for predicting BOD concentration in Hwangji station, South Korea.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.87-101
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• 2022

Cyber-attacks occur in the blink of an eye in cyberspace, and the damage is increasing all over the world. Therefore, it is necessary to develop a cyber common operational picture that can grasp the various assets belonging to the 3rd layer of cyberspace from various perspectives. By applying the method for grasping battlefield information used by the military, it is possible to achieve optimal cyberspace situational awareness. Therefore, in this study, the visualization screens necessary for the cyber common operational picture are identified and the criteria (response speed, user interface, object symbol, object size) are investigated. After that, the framework is designed by applying the identified and investigated items, and the visualization screens are implemented accordingly. Finally, among the criteria investigated by the visualization screen, an experiment is conducted on the response speed that cannot be recognized by a photograph. As a result, all the implemented visualization screens met the standard for response speed. Such research helps commanders and security officers to build a cyber common operational picture to prepare for cyber-attacks.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.3
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• pp.185-194
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• 2022

Coffee is one of the most consumed beverages in the world and is the second largest traded commodity after petroleum. Due to the great demand of this product, large amounts of waste is generated in the coffee industry, which are toxic and represent serious environmental problems. This study aims to study the possibility of recycling spent coffee grounds (SCG) as a mineral admixture by replacing the cement in the manufacturing of concrete. To recycle the coffee g rounds, the SCG was dried to remove moisture and fired in a kiln at 850 ℃ for 8 hours. Carbonized coffee grounds are produced as coffee grounds ash (CGA) through ball mill grinding. The chemical composition of the prepared coffee grounds ash was investigated using X-ray fluorescence (XFR). According to the chemical composition analysis, the major elements of coffee grounds ash are K₂O(51.74 %), CaO(15.92 %), P₂O₅(14.39 %), MgO(7.74 %) and SO₃(6.89 %), with small amounts of F₂O₃(0.66 %), SiO₂(0.59 %) and Al₂O₃(0.31 %) content. To evaluate quality and mechanical properties, substitutions of 5, 10, and 15 wt.% of coffee grounds ash (CGA) were tested. From the quality test results, the 28-day activity index of CGA5 reached 80 %, and the flow value ratio reached 96 %, which is comparable to the minimum requirement for second-grade FA. From the test results of the mortar, the optimal results have been found in specimens with 5 wt-% coffee grounds ash, showing good mechanical and physical properties.

• Microbiology and Biotechnology Letters
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• v.49 no.1
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• pp.75-87
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• 2021

Type I Interferons (IFNα) are known for their role as biological anticancer agents owing to their cell-apoptosis inducing properties. Development of an appropriate, cost-effective host expression system is crucial for meeting the increasing demand for proteins. Therefore, this study aims to develop codon-optimized IFNα-2b in L. lactis NZ3900. These cells express extracellular protein using the NICE system and Usp₄₅ signal peptide. To validate the mature form of the expressed protein, the recombinant IFNα-2b was screened in a human colorectal cancer cell line using the cytotoxicity assay. The IFNα-2b was successfully cloned into the pNZ8148 vector, thereby generating recombinant L. lactis pNZ8148-SP_Usp45-IFNα-2b. The computational analysis of codon-optimized IFNα-2b revealed no mutation and amino acid changes; additionally, the codon-optimized IFNα-2b showed 100% similarity with native human IFNα-2b, in the BLAST analysis. The partial size exclusion chromatography (SEC) of extracellular protein yielded a 19 kDa protein, which was further confirmed by its positive binding to anti-IFNα-2b in the western blot analysis. The crude protein and SEC-purified partial fraction showed IC₅₀ values of 33.22 ㎍/ml and 127.2 ㎍/ml, respectively, which indicated better activity than the metabolites of L. lactis NZ3900 (231.8 ㎍/ml). These values were also comparable with those of the regular anticancer drug tamoxifen (105.5 ㎍/ml). These results demonstrated L. lactis as a promising host system that functions by utilizing the pNZ8148 NICE system. Meanwhile, codon-optimized usage of the inserted gene increased the optimal protein expression levels, which could be beneficial for its large-scale production. Taken together, the recombinant L. lactis IFNα-2b is a potential alternative treatment for colorectal cancer. Furthermore, its activity was analyzed in the WiDr cell line, to assess its colorectal anticancer activities in vivo.

• Journal of Conservation Science
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• v.36 no.6
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• pp.505-510
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• 2020

In this study, the anti-stain effect of the Traditional Myoung-oil, which has been reproduced through traditional method, the Clean Myoung-oil, which was developed in an eco-friendly method through scientific analysis of Traditional Myoung-oil, and the perilla oil, which is the raw material of Myoung-oil and is currently used as a finishing agent when repairing wooden cultural properties was evaluated. As a result of the evaluation, perilla oil showed almost no anti-stain effect, whereas both types of Myoung-oil showed high anti-stain effect. However, it was confirmed that the anti-stain effect was significantly reduced after 4 weeks of exposure to the strain when Myoung-oil was diluted with terpene oil, a natural solvent. Thus, it was considered that the amount of treatment in the wood affected the anti-stain effect of Myoung-oil. In other words, in constructing wooden buildings, Myoung-o il is more suitable as a finishing agent to suppress mold growth than perilla oil. And, in order to increase the applicability of Myoung-oil, it is suggested that additional research on the optimal treatment amount and treatment method that can inhibit mold growth inhibition in outdoor environments is necessary.