• Ultrasonography
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• v.32 no.1
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• pp.59-65
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• 2013

Purpose: The purpose of this study is to establish the methodology regarding synthesis of ultrasound contrast agent imaging, and to evaluate the characteristics of the synthesized ultrasound contrast agents, including size or degradation interval and image quality. Materials and Methods: The ultrasound contrast agent, composed of liposome and SF6, was synthesized from the mixture solution of $21{\mu}mol$ DPPC (1, 2-Dihexadecanoyl-sn-glycero-3-phosphocholine, $C_{40}H_{80}NO_8P$), $9{\mu}mol$ cholesterol, $1.9{\mu}mol$ of DCP (Dihexadecylphosphate, $[CH_3(CH_2)_{15}O]_2P(O)OH$), and chloroform. After evaporation in a warm water bath and drying during a period of 12-24 hours, the contrast agent was synthesized by the sonication process by addition of buffer and SF6 gas. The size of the contrast agent was controlled by use of either extruder or sonication methods. After synthesis of contrast agents, analysis of the size distribution of the bubbles was performed using dynamic light scattering measurement methods. The degradation curve was also evaluated by changes in the number of contrast agents via light microscopy immediate, 12 hours, 24 hours, 36 hours, 48 hours, 60 hours, 72 hours, and 84 hours after synthesis. For evaluation of the role as an US contrast agent, the echogenicity of the synthesized microbubble was compared with commercially available microbubbles (SonoVue, Bracco, Milan, Italy) using a clinical ultrasound machine and phantom. Results: The contrast agents were synthesized successfully using an evaporation-drying-sonication method. The majority of bubbles showed a mean size of 154.2 nanometers, and they showed marked degradation 24 hours after synthesis. ANOVA test revealed a significant difference among SonoVue, synthesized contrast agent, and saline (p < 0.001). Although no significant difference was observed between SonoVue and the synthesized contrast agent, difference in echogenicity was observed between synthesized contrast agent and saline (p < 0.01). Conclusion: We could synthesize ultrasound contrast agents using an evaporation-drying-sonication method. On the basis of these results, many prospective types of research, such as anticancer drug delivery, gene delivery, including siRNA or microRNA, targeted molecular imaging, and targeted therapy can be performed.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.323-334
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• 2007

We analyzed the ocean environmental data from water sample and automatic measurement instruments with the Incheon-Jeju passenger ship for 18 times during 4 years from 2001 to 2004. The objectives of this study are to monitor the spatial and temporal variations of ocean environmental parameters in coastal waters of the Yellow Sea using water sample analysis, and to compare and analyze the reliability of automatic measurement sensors for chlorophyll and turbidity using in situ measurements. The chlorophyll concentration showed the ranges between 0.1 to $6.0mg/m^3$. High concentrations occurred in the Gyeonggi Bay through all the cruises. The maximum value of chlorophyll concentration was $16.5mg/m^3$ in this area during September 2004. The absorption coefficients of dissolve organic matter at 400 nm showed below $0.5m^{-1}$ except those in August 2001 During 2002-2003, it did not distinctly change the seasonal variations with the ranges 0.1 to $0.4m^{-1}$. In the case of suspended sediment (SS) concentration, most of the area showed below $20g/m^3$ through all seasons except the Gyeonggi Bay and around Mokpo area. In general SS concentration of autumn and winter season was higher than that of summer. The central area of the Yellow Sea appeared to have lower value $10g/m^3$. The YSI fluorometer for chlorophyll concentration had a very low reliability and turbidity sensor had a $R^2$ value of 0.77 through the 4 times measurements comparing with water sampling method. For the automatic measurement using instruments for chlorphlyll and suspended sediment concentration, McVan and Choses sensor was greater than YSI multisensor. The SeaWiFS SS distribution map was well spatially matched with in situ measurement, however, there was a little difference in quantitative concentration.

• Journal of IKEEE
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• v.25 no.3
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• pp.493-500
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• 2021

In this paper, we propose the development of deep learning structure to improve quality of polygonal containers. The deep learning structure consists of a convolution layer, a bottleneck layer, a fully connect layer, and a softmax layer. The convolution layer is a layer that obtains a feature image by performing a convolution 3x3 operation on the input image or the feature image of the previous layer with several feature filters. The bottleneck layer selects only the optimal features among the features on the feature image extracted through the convolution layer, reduces the channel to a convolution 1x1 ReLU, and performs a convolution 3x3 ReLU. The global average pooling operation performed after going through the bottleneck layer reduces the size of the feature image by selecting only the optimal features among the features of the feature image extracted through the convolution layer. The fully connect layer outputs the output data through 6 fully connect layers. The softmax layer multiplies and multiplies the value between the value of the input layer node and the target node to be calculated, and converts it into a value between 0 and 1 through an activation function. After the learning is completed, the recognition process classifies non-circular glass bottles by performing image acquisition using a camera, measuring position detection, and non-circular glass bottle classification using deep learning as in the learning process. In order to evaluate the performance of the deep learning structure to improve quality of polygonal containers, as a result of an experiment at an authorized testing institute, it was calculated to be at the same level as the world's highest level with 99% good/defective discrimination accuracy. Inspection time averaged 1.7 seconds, which was calculated within the operating time standards of production processes using non-circular machine vision systems. Therefore, the effectiveness of the performance of the deep learning structure to improve quality of polygonal containers proposed in this paper was proven.

• Journal of the Korean Applied Science and Technology
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• v.35 no.4
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• pp.1108-1119
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• 2018

As the interest on the air pollution is gradually rising at home and abroad, automotive and fuel researchers have been studied on the exhaust and greenhouse gas emission reduction from vehicles through a lot of approaches, which consist of new engine design, innovative after-treatment systems, using clean (eco-friendly alternative) fuels and fuel quality improvement. This research has brought forward two main issues : exhaust emissions (regulated and non-regulated emissions, PM particle matter) and greenhouse gases of vehicle. Exhaust emissions and greenhouse gases of automotive had many problem such as the cause of ambient pollution, health effects. In order to reduce these emissions, many countries are regulating new exhaust gas test modes. Worldwide harmonized light-duty vehicle test procedure (WLTP) for emission certification has been developed in WP.29 forum in UNECE since 2007. This test procedure was applied to domestic light duty diesel vehicles at the same time as Europe. The air pollutant emissions from light-duty vehicles are regulated by the weight per distance, which the driving cycles can affect the results. Exhaust emissions of vehicle varies substantially based on climate conditions, and driving habits. Extreme outside temperatures tend to increasing the emissions, because more fuel must be used to heat or cool the cabin. Also, high driving speeds increases the emissions because of the energy required to overcome increased drag. Compared with gradual vehicle acceleration, rapid vehicle acceleration increases the emissions. Additional devices (air-conditioner and heater) and road inclines also increases the emissions. In this study, three light-duty vehicles were tested with WLTP, NEDC, and FTP-75, which are used to regulate the emissions of light-duty vehicles, and how much emissions can be affected by different driving cycles. The emissions gas have not shown statistically meaningful difference. The maximum emission gas have been found in low speed phase of WLTP which is mainly caused by cooled engine conditions. The amount of emission gas in cooled engine condition is much different as test vehicles. It means different technical solution requires in this aspect to cope with WLTP driving cycle.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.63-72
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• 2019

As a sub-concept of corporate or organization governance, business governance and IT governance have become major research topics in academia. However, despite the importance of process as a construct for mediating the domain between business and information technology, research on process governance is relatively inadequate. Process Governance focuses on activities that link business strategy with IT system implementation and explains the creation of corporate core values. The researcher studied the basic conceptual governance models of political science, sociology, public administration, and classified governance styles into six categories. The researcher focused on the series of metamodels. For examples, the traditional Strategy Alignment Model(SAM) by Henderson and Venkatraman which is replaced by the neo-SAM model, organizational governance network model, sequential organization governance model, organization governance meta model, process governance CUBE model, COSO and process governance CUBE comparison model, and finally Process Governance Framework and etc. The Major difference between SAM and neo-SAM model is Process Governance domain inserted between Business Governance and IT Governance. Among several metamodels, Process Governance framework, the core conceptual model consists of four activity dimensions: strategic aligning, human empowering, competency enhancing, and autonomous organizing. The researcher designed five variables for each activity dimensions, totally twenty variables. Besides four activity dimensions, there are six driving forces for Process Governance cycle: De-normalizing power, micro-power, vitalizing power, self-organizing power, normalizing power and sense-making. With four activity dimensions and six driving powers, an organization can maintain the flexibility of process governance cycle to cope with internal and external environmental changes. This study aims to propose the Process Governance competency model and Process Governance variables. The situation of the industry is changing from the function-oriented organization management to the process-oriented perspective. Process Governance framework proposed by the researcher will be the contextual reference models for the further diffusion of the research on Process Governance domain and the operational definition for the development of Process Governance measurement tools in detail.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.44 no.4
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• pp.363-373
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• 2018

In recent years, parabens used as preservatives in cosmetics have become a problem of human safety. Therefore, in this study, we tried to evaluate the preservative efficacy of 1,3-butylene glycol, 1,2-hexanediol, and 1,2-pentanediol as a preservative system to replace parabens. 1,3-Butylene glycol was added to cosmetic creams at a concentration of between 5 and 25%. The preservative efficacy of 1,3-butylene glycol was determined using a M-3 challenge test, as recommended by the Personal Care Products Council (formally CTFA). The alkane diols, such as 1,2-hexanediol and 1,2-pentanediol, were assessed in a similar manner. An evaluation of the preservative efficacy of 1,3-butylene glycol revealed that it was effective against all tested microbial strains at a concentration of 25%. We also investigated the efficacy of combinations of 0.3% phenoxyethanol and 0.1% ethylhexylglycerin. Finally, we tested the alkane diols, including 1,2-hexanediol and 1,2-pentanediol, as an alternative to the preservative 0.3% phenoxyethanol. Both 1% 1,2-hexanediol and 1% 1,2-pentanediol demonstrated preservative efficacy. Taken together, our study demonstrated that the formulation of 25% 1,3-butylene glycol and 0.1% ethylhexylglycerin, 1% 1,2-hexanediol, and 1% 1,2-pentanediol had the best preservative efficacy of the compositions tested. Thus, this study suggests that the formulation is a possibility of substituting parabens preservatives, which has been used in cosmetics and has become a safety issue.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.33-49
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• 2018

The impact of TV Drama success on TV Rating and the channel promotion effectiveness is very high. The cultural and business impact has been also demonstrated through the Korean Wave. Therefore, the early prediction of the blockbuster success of TV Drama is very important from the strategic perspective of the media industry. Previous studies have tried to predict the audience ratings and success of drama based on various methods. However, most of the studies have made simple predictions using intuitive methods such as the main actor and time zone. These studies have limitations in predicting. In this study, we propose a model for predicting the popularity of drama by analyzing the customer's viewing pattern based on various theories. This is not only a theoretical contribution but also has a contribution from the practical point of view that can be used in actual broadcasting companies. In this study, we collected data of 280 TV mini-series dramas, broadcasted over the terrestrial channels for 10 years from 2003 to 2012. From the data, we selected the most highly ranked and the least highly ranked 45 TV drama and analyzed the viewing patterns of them by 11-step. The various assumptions and conditions for modeling are based on existing studies, or by the opinions of actual broadcasters and by data mining techniques. Then, we developed a prediction model by measuring the viewing-time distance (difference) using Euclidean and Correlation method, which is termed in our study similarity (the sum of distance). Through the similarity measure, we predicted the success of dramas from the viewer's initial viewing-time pattern distribution using 1~5 episodes. In order to confirm that the model is shaken according to the measurement method, various distance measurement methods were applied and the model was checked for its dryness. And when the model was established, we could make a more predictive model using a grid search. Furthermore, we classified the viewers who had watched TV drama more than 70% of the total airtime as the "passionate viewer" when a new drama is broadcasted. Then we compared the drama's passionate viewer percentage the most highly ranked and the least highly ranked dramas. So that we can determine the possibility of blockbuster TV mini-series. We find that the initial viewing-time pattern is the key factor for the prediction of blockbuster dramas. From our model, block-buster dramas were correctly classified with the 75.47% accuracy with the initial viewing-time pattern analysis. This paper shows high prediction rate while suggesting audience rating method different from existing ones. Currently, broadcasters rely heavily on some famous actors called so-called star systems, so they are in more severe competition than ever due to rising production costs of broadcasting programs, long-term recession, aggressive investment in comprehensive programming channels and large corporations. Everyone is in a financially difficult situation. The basic revenue model of these broadcasters is advertising, and the execution of advertising is based on audience rating as a basic index. In the drama, there is uncertainty in the drama market that it is difficult to forecast the demand due to the nature of the commodity, while the drama market has a high financial contribution in the success of various contents of the broadcasting company. Therefore, to minimize the risk of failure. Thus, by analyzing the distribution of the first-time viewing time, it can be a practical help to establish a response strategy (organization/ marketing/story change, etc.) of the related company. Also, in this paper, we found that the behavior of the audience is crucial to the success of the program. In this paper, we define TV viewing as a measure of how enthusiastically watching TV is watched. We can predict the success of the program successfully by calculating the loyalty of the customer with the hot blood. This way of calculating loyalty can also be used to calculate loyalty to various platforms. It can also be used for marketing programs such as highlights, script previews, making movies, characters, games, and other marketing projects.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.63-88
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• 2019

The goal of this paper is to investigate changes in North Korea's domestic and foreign policies through automated text analysis over North Korea represented in South Korean mass media. Based on that data, we then analyze the status of text mining research, using a text mining technique to find the topics, methods, and trends of text mining research. We also investigate the characteristics and method of analysis of the text mining techniques, confirmed by analysis of the data. In this study, R program was used to apply the text mining technique. R program is free software for statistical computing and graphics. Also, Text mining methods allow to highlight the most frequently used keywords in a paragraph of texts. One can create a word cloud, also referred as text cloud or tag cloud. This study proposes a procedure to find meaningful tendencies based on a combination of word cloud, and co-occurrence networks. This study aims to more objectively explore the images of North Korea represented in South Korean newspapers by quantitatively reviewing the patterns of language use related to North Korea from 2016. 11. 1 to 2019. 5. 23 newspaper big data. In this study, we divided into three periods considering recent inter - Korean relations. Before January 1, 2018, it was set as a Before Phase of Peace Building. From January 1, 2018 to February 24, 2019, we have set up a Peace Building Phase. The New Year's message of Kim Jong-un and the Olympics of Pyeong Chang formed an atmosphere of peace on the Korean peninsula. After the Hanoi Pease summit, the third period was the silence of the relationship between North Korea and the United States. Therefore, it was called Depression Phase of Peace Building. This study analyzes news articles related to North Korea of the Korea Press Foundation database(www.bigkinds.or.kr) through text mining, to investigate characteristics of the Kim Jong-un regime's South Korea policy and unification discourse. The main results of this study show that trends in the North Korean national policy agenda can be discovered based on clustering and visualization algorithms. In particular, it examines the changes in the international circumstances, domestic conflicts, the living conditions of North Korea, the South's Aid project for the North, the conflicts of the two Koreas, North Korean nuclear issue, and the North Korean refugee problem through the co-occurrence word analysis. It also offers an analysis of South Korean mentality toward North Korea in terms of the semantic prosody. In the Before Phase of Peace Building, the results of the analysis showed the order of 'Missiles', 'North Korea Nuclear', 'Diplomacy', 'Unification', and ' South-North Korean'. The results of Peace Building Phase are extracted the order of 'Panmunjom', 'Unification', 'North Korea Nuclear', 'Diplomacy', and 'Military'. The results of Depression Phase of Peace Building derived the order of 'North Korea Nuclear', 'North and South Korea', 'Missile', 'State Department', and 'International'. There are 16 words adopted in all three periods. The order is as follows: 'missile', 'North Korea Nuclear', 'Diplomacy', 'Unification', 'North and South Korea', 'Military', 'Kaesong Industrial Complex', 'Defense', 'Sanctions', 'Denuclearization', 'Peace', 'Exchange and Cooperation', and 'South Korea'. We expect that the results of this study will contribute to analyze the trends of news content of North Korea associated with North Korea's provocations. And future research on North Korean trends will be conducted based on the results of this study. We will continue to study the model development for North Korea risk measurement that can anticipate and respond to North Korea's behavior in advance. We expect that the text mining analysis method and the scientific data analysis technique will be applied to North Korea and unification research field. Through these academic studies, I hope to see a lot of studies that make important contributions to the nation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.3
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• pp.208-220
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• 2019

The seasonality of leaf fall has important implications for understanding the response of trees' phenology to climate change. In this study, we quantified the leaf fall pattern with a model to estimate the timing and speed of leaf litter according to species and considered the nutrient use strategy of canopy species. In the autumns of 2015 and 2016, leaf litter was collected periodically using 36 litter-traps from the deciduous forests in Gwangneung and sorted by species. The seasonal leaf fall pattern was estimated using the non-linear regression model of Dixon. Additionally, the resorption rate was calculated by analyzing the nitrogen concentration of the leaf litter at each collection time. The leaf litter generally began in early October and ended in mid-November depending on the species. At the peak time (T50) of leaf fall, on average, Carpinus laxiflora was first, and Quercus serrata was last. The rate of leaf fall was fastest (18.6 days) for Sorbus alnifolia in 2016 and slowest (40.8 days) for C. cordata in 2015. The nitrogen resorption rates at T50 were 0.45% for Q. serrata and 0.48% for C. laxiflora, and the resorption rate in 2015 with less precipitation was higher than in 2016. Since falling of leaf litter is affected by environmental factors such as temperature, precipitation, photoperiod, and $CO_2$ during the period attached foliage, the leaf fall pattern and nitrogen resorption differed year by year depending on the species. If we quantify the fall phenomena of deciduous trees and analyze them according to various conditions, we can predict whether the changes in leaf fall timing and speed due to climate change will prolong or shorten the growth period of trees. In addition, it may be possible to consider how this affects their nutrient use strategy.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.3
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• pp.73-84
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• 2019

Despite decades of work experience, workers at small- and medium-sized enterprises(SME) here have yet to make inroads into the self-employed sector that utilizes the job competency they have accumulated at work after retirement. Unlike large companies, SME do not have a proper system for improving the long-term job competency of their employees as they focus on their immediate performance. It is necessary to analyse the independent variables affecting the job competency of employees of SME to derive practical implications for the personnel of SME. In the preceding studies, there are independent variable analyses that affect job competency in specialized industries, such as health care, public officials and IT, but the analysis of workers at SME is insufficient. This study set the person-job fit and organizational justice based on the prior studies of the independent variables that affect the job competency of SME general workers as a dependent variable. The sub-variables of each variable derived knowledge, skills, experience, and desire for person-job fit, and distribution, procedural and deployment justice for organizational justice, respectively. The survey of employees of SME in Korea was conducted from February to March 2019 by Likert 5 scales, and the survey was retrieved from 323 people and analyzed in a demonstration using the SPSS and AMOS statistics package. Among the four sub-independent variables of person-job fit, knowledge, skills and experience were shown to have a significant impact on the job competency, and desire was not shown to be so. Among the three sub-independent variables of organizational justice, deployment justice has a significant impact on job competency, but distribution and procedural justices have not. Personnel managers of SME need to improve the job competency of their employees by appropriately utilizing independent variables such as knowledge, skills, experience and deployment at each stage, including recruitment, deployment, and promotion. Future job competency modeling studies are needed to overcome the limitations of this study, which fails to objectively measure job competency.