• Journal of Chest Surgery
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• v.36 no.4
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• pp.219-228
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• 2003

Background:Liquid nitrogen freezing techniques have already met with widespread success in biology and medicine as a means of long-term storage for cells and tissues. The use of cryoprotectants such as glycerol and dimethylsulphoxide to prevent ice crystal formation, with carefully controlled rates of freezing and thawing, allows both structure and viability to be retained almost indefinitely. Cryopreservation of various tissues has various con-trolled rates of freezing. Material and Method: To find the optimal freezing curve and the chamber temperature, we approached the thermodynamic calculation of tissues in two ways. One is the direct calculation method. We should know the thermophysical characteristics of all components, latent heat of fusion, area, density and volume, etc. This kind of calculation is so sophisticated and some variables may not be determined. The other is the indirect calculation method. We performed the tissue freezing with already used freezing curve and we observed the actual freezing curve of that tissue. And we modified the freezing curve with several steps of calculation, polynomial regression analysis, time constant calculation, thermal response calculation and inverse calculation of chamber temperature. Result: We applied that freezing program on mesenchymal stem cell, chondrocyte, and osteoblast. The tissue temperature decreased according to the ideal freezing curve without temperature rising. We did not find any differences in survival. The reason is postulated to be that freezing material is too small and contains cellular components. We expect the significant difference in cellular viability if the freezing curve is applied on a large scale of tissues. Conclusion: This program would be helpful in finding the chamber temperature for the ideal freezing curie easily.

• Food Engineering Progress
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• v.21 no.2
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• pp.167-173
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• 2017

Purple-fleshed potato powder (PFPP) was investigated to determine optimal mixing ratio with milk powder and dextrin to produce a ready-to-eat mashed potato powder. The rheological characteristics, color, and anthocyanin contents were studied at a different concentration of ingredients. The power-law model was applied to explain the mechanical spectra of mashed potatoes which represented the change in structure induced by different mixing ratios. Mixture design was used to obtain the experimental points used to establish the empirical models to describe the effects of each ingredient on the characteristic of the mashed potato. The results of mechanical spectra showed that both storage and loss moduli (G' and G'') were significantly influenced by PFPP and milk powder concentration. The power law parameters n' and n'' showed higher values for the mashed potato with a lower concentration of PFPP and a higher concentration of milk powder, which showed that the gel networks involved in the mashed potato were weaker. The optimum mixing ratio with the highest redness and anthocyanin content, while maintaining the rheological properties similar to the commercial mashed potato, was determined as PFPP:milk powder:dextrin = 90.49:4.86:4.65 (w/w). The proportions of PFPP and milk powder in the formulation significantly changed the characteristics of mashed potato, whereas no significant effect of dextrin was observed in this formulation.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.903-911
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• 2022

Groundwater, one of the resources for supplying water, fluctuates in water level due to various natural factors. Recently, research has been conducted to predict fluctuations in groundwater levels using Artificial Neural Network (ANN). Previously, among operators in ANN, Gradient Descent (GD)-based Optimizers were used as Optimizer that affect learning. GD-based Optimizers have disadvantages of initial correlation dependence and absence of solution comparison and storage structure. This study developed Gradient Descent combined with Harmony Search (GDHS), a new Optimizer that combined GD and Harmony Search (HS) to improve the shortcomings of GD-based Optimizers. To evaluate the performance of GDHS, groundwater level at Icheon Yullhyeon observation station were learned and predicted using Multi Layer Perceptron (MLP). Mean Squared Error (MSE) and Mean Absolute Error (MAE) were used to compare the performance of MLP using GD and GDHS. Comparing the learning results, GDHS had lower maximum, minimum, average and Standard Deviation (SD) of MSE than GD. Comparing the prediction results, GDHS was evaluated to have a lower error in all of the evaluation index than GD.

• Applied Microscopy
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• v.31 no.2
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• pp.207-214
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• 2001

Anatomy and ultrastructure of the modifeid Krana pattern have been studied in succulent Salsola collina Pall. Cylindrical leaves exhibited the Salsoloid Kranz type containing two layers of peripheral chlorenchyma that surrounded the water storage cells and vascular tissues. Small veins were also peripherally arranged, but mostly embedded in the vicinity of the inner chlorenchma without the orderly arrangement of the concentric layering of bundle sheath and mesophyll cells. The current study mainly focused on the chlorenchyma tissue abutting such minor veins. The outer columnar layer exhibited features similar to the characteristics of palisade mesophyll cells, while the inner cuboid layer to the bundle sheath cells of a typical $C_4$ Kranz pattern. Cellular components of the inner chlorenchyma were centripetal and numerous, but starch-laden chloroplasts were rudimentary in the thylakoidal system. The outer chlorenchyma demonstrated normally developed chloroplasts having well-stacked thylakoids and plastoglobuli. Branched and complicated plasmodesmata frequently occurred in thick interfaces of the two layers, implying the active movement of the photosynthates between them. The present data were mostly congruent with one of the structural features of the C4 subtypes , NADP-ME type, reported in the $C_4$ pattern. The Kranz pattern encountered in this Salsola probably has been directly related to the structural modification that occurred during a functional adaptation to the $C_4$ photosynthesis.

• Korean Journal of Food Science and Technology
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• v.31 no.3
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• pp.612-618
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• 1999

Physicochemical properties of amaranth starch were compared with those of waxy rice and waxy corn starches. Amaranth starch granules vary from $1.1{\sim}1.9\;{\mu}m$ in diameter and are polygonal in shape. Total amylose contents from waxy rice, waxy corn and amaranth starches were 0.01, 0.03 and 0.07%, respectively. Swelling power of amaranth starch granule was slightly different from waxy rice. The swelling power of amaranth increased at $70^{\circ}C$. X-ray diffraction patterns of amaranth and other waxy cereal starches showed an A-type crystalline structure. Relative crystallinities of their starches were similar. According to pasting properties by Rapid Visco-Analyzer, amaranth starch showed a very high gelatinization temperature $(75.1^{\circ}C)$ and lower viscosity and higher stability than other waxy cereal starches during heating and cooling cycle. Peak onset temperatures (To) of starches from waxy rice, waxy corn and amaranth in DSC thermograms were $58.7{\sim}64.0$, 67.2 and $71.5^{\circ}C$, respectively, and their peak enthalpies were similar. Enthalpy of reheated amaranth starch after 3 day storage at $4^{\circ}C$ was higher than those of waxy corn and rices starchs.

• KEPCO Journal on Electric Power and Energy
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• v.1 no.1
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• pp.175-180
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• 2015

A facile soft chemical synthesis route is used to grow nano-buds of copper hydroxide [$Cu(OH)_2$] thin films on stainless steel substrate[SS]. Besides different chemical methods for synthesis of $Cu(OH)_2$ nanostructure, the chemical bath deposition (CBD) is attractive for its simplicity and environment friendly condition. The structural, morphological, and electro-chemical properties of $Cu(OH)_2$ thin films are studied by means of X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy (FESEM), cyclic voltammetry (CV) and galvanostatic charge-discharge (GCD) measurement techniques. The results showed that, facile chemical synthesis route allows to form the polycrystalline, granular nano-buds of $Cu(OH)_2$ thin films. The electrochemical properties of $Cu(OH)_2$ thin films are studied in an aqueous 1 M KOH electrolyte using cyclic voltammetry. The sample exhibited supercapacitive behavior with $340Fg^{-1}$ specific capacitance. Moreover, electrochemical capacitive measurements of $Cu(OH)_2/SS$ electrode exhibit a high specific energy and power density about ${\sim}83Wh\;kg^{-1}$ and ${\sim}3.1kW\;kg^{-1}$, respectively, at $1mA\;cm^{-2}$ current density. The superior electrochemical properties of copper hydroxide ($Cu(OH)_2/SS$) electrode with nano-buds like structure mutually improves pseudocapacitive performance. This work evokes scalable chemical synthesis with the enhanced supercapacitive performance of $Cu(OH)_2/SS$ electrode in energy storage devices.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.4B
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• pp.323-338
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• 2003

The main memory database systems (MMDBMS) efficiently supports various database applications that require high performance since it employs main memory rather than disk as a primary storage. Recently, it has been increased needs that have the fast data processing as well as the efficient modeling of application requiring for a complicated structure, and conformity to applications that need the strict dta consistency. In MMDBMS, because all the data is located in the main memory, it can support the usable expression methods of data satisfying their needs without performance overhead. The method has the operation to manipulate the data and the constraint such as referential integrity in more detail. The data model consists of this methods is an essential component to decide the expression power of DBMS. In this paper, we discuss about various requests to provide the communication services and propose the data model that support it. The mainly discussed issues are 1) definition of the relationship between tables using the pointer, 2) navigation of the data using the relationship, 3) support of the referential integrity for pointer, 4) support of the uniform processing time for the join, 5) support of the object-oriented concepts, and 6) sharing of an index on multi-tables. We discuss the pointer-based data model that designed to include these issues to efficiently support complication environments.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.164-175
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• 2019

The Tor Tong Daeng Irrigation Project with the irrigation area of 61,400 hectares is located in the Ping Basin of the Upper Central Plain of Thailand where farmers depended on both surface water and groundwater. In the drought year, water storage in the Bhumipol Dam is inadequate to allocate water for agriculture, and caused water deficit in many irrigation projects. Farmers need to find extra sources of water such as water from farm pond or groundwater as a supplement. The operation of Bhumipol Dam and irrigation demand estimation are vital for irrigation water allocation to help solve water shortage issue in the irrigation project. The study aims to determine the smart dam operation system to mitigate water shortage in this irrigation project via introduction of machine learning to improve dam operation and irrigation demand estimation via soil moisture estimation from satellite images. Via ANN technique application, the inflows to the dam are generated from the upstream rain gauge stations using past 10 years daily rainfall data. The input vectors for ANN model are identified base on regression and principal component analysis. The structure of ANN (length of training data, the type of activation functions, the number of hidden nodes and training methods) is determined from the statistics performance between measurements and ANN outputs. On the other hands, the irrigation demand will be estimated by using satellite images, LANDSAT. The Enhanced Vegetation Index (EVI) and Temperature Vegetation Dryness Index (TVDI) values are estimated from the plant growth stage and soil moisture. The values are calibrated and verified with the field plant growth stages and soil moisture data in the year 2017-2018. The irrigation demand in the irrigation project is then estimated from the plant growth stage and soil moisture in the area. With the estimated dam inflow and irrigation demand, the dam operation will manage the water release in the better manner compared with the past operational data. The results show how smart system concept was applied and improve dam operation by using inflow estimation from ANN technique combining with irrigation demand estimation from satellite images when compared with the past operation data which is an initial step to develop the smart dam operation system in Thailand.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1172-1179
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• 2022

This paper describes a design of point scalar multiplier for public-key cryptography based on binary Edwards curves (BEdC). For efficient implementation of point addition (PA) and point doubling (PD) on BEdC, projective coordinate was adopted for finite field arithmetic, and computational performance was improved because only one inversion was involved in point scalar multiplication (PSM). By applying optimizations to hardware design, the storage and arithmetic steps for finite field arithmetic in PA and PD were reduced by approximately 40%. We designed two types of point scalar multipliers for BEdC, Type-I uses one 257-b×257-b binary multiplier and Type-II uses eight 32-b×32-b binary multipliers. Type-II design uses 65% less LUTs compared to Type-I, but it was evaluated that it took about 3.5 times the PSM computation time when operating with 240 MHz. Therefore, the BEdC crypto core of Type-I is suitable for applications requiring high-performance, and Type-II structure is suitable for applications with limited resources.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.177-188
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• 2022

Recently, the application of distributed acoustic sensors (DAS), which can replace geophones and seismometers, has significantly increased along with interest in micro-seismic monitoring technique, which is one of the CO₂ storage monitoring techniques. A significant amount of temporally and spatially continuous data is recorded in a DAS monitoring system, thereby necessitating fast and accurate data processing techniques. Because event detection and seismic phase picking are the most basic data processing techniques, they should be performed on all data. In this study, a machine learning-based P, S wave phase picking algorithm was developed to compensate for the limitations of conventional phase picking algorithms, and it was modified using a transfer learning technique for the application of DAS data consisting of a single component with a low signal-to-noise ratio. Our model was constructed by modifying the convolution-based EQTransformer, which performs well in phase picking, to the ResUNet structure. Not only the global earthquake dataset, STEAD but also the augmented dataset was used as training datasets to enhance the prediction performance on the unseen characteristics of the target dataset. The performance of the developed algorithm was verified using K-net and KiK-net data with characteristics different from the training data. Additionally, after modifying the trained model to suit DAS data using the transfer learning technique, the performance was verified by applying it to the DAS field data measured in the Pohang Janggi basin.