• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.74-83
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• 2018

This study analyzed the differences in effectiveness between celebrity and consumer endorsers by type of story in storytelling ads. A $2{\times}2$ factorial design experiment was conducted: the type of advertising endorser (celebrity, consumer) and the type of story (life experience, brand myth). 200 women in their 30s and 40s participated in an online survey. Study shows that for a life-experience ad, the consumer model has higher advertising effectiveness than the celebrity endorser for all three dependent variables : ad attitude, brand attitude, and purchase intent. In the case of brand-myth ad, the celebrity endorser had more favorable ad attitude than the consumer endorser did, while there was no difference in brand attitude and purchase intent. The theoretical and practical implications of the study were discussed in the conclusion.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.199-216
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• 2024

This research examines the factors influencing consumer purchase intentions in e-commerce live streaming, set against the backdrop of the internet celebrity economy. The investigation serves as a pivotal inquiry into the dynamics of this economy, striving to uncover the extent of internet celebrities' influence, particularly in terms of their economic impact. Employing the Emotional Behavioral Cognitive (ABC) attitude theory and the Stimulus Organism Response (S-O-R) theory as foundational frameworks, this study scrutinizes internet celebrity live streaming sales. It incorporates direct observations and leverages existing scholarly work to devise a tailored measurement scale and questionnaire. From this, a research model and hypotheses are developed, leading to the establishment of an empirical model. This empirical model is instrumental in statistically analyzing how e-commerce live streaming, within the internet celebrity economy context, shapes consumer purchase intentions. By integrating theoretical insights and empirical findings, the research elucidates the strategic dimensions and consumer behavior aspects in digital commerce. It enhances understanding of how internet celebrity influence intersects with consumer purchasing processes. Overall, this study contributes to the academic discourse on digital marketing and consumer behavior, providing a nuanced perspective on the mechanisms through which internet celebrities affect e-commerce. It offers valuable implications for marketers, strategists, and policymakers aiming to navigate the complex landscape of the internet celebrity economy.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.103-119
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• 2018

This study explored news coverage on the sex crimes and analyzed news by network analysis and frame analysis based on the layered model to compare news coverage on the celebrity. As a result, in case of celebrity the broadcasting focused more and the tone of news is more sensational. The news in ground wave broadcasting more detailed on the sex crimes. It blamed the An, the governor of Chungnam more and the news is more sensational by interviewing marginal man. In #Metoo case, broadcasting news focused on the offender. The title of case name and the headline are framed based on the offender. Especially consensual relationship frame is dominated in the sex crime news. This study also can see the offender blaming frame and in the viewpoint of agenda-setting. It is difficult to find the cause of #Metoo movement and the structural approach on the case. This study highlighted the importance of layed model when analyzing the sex-crime news related with #Metoo movement.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.99-106
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• 2020

The purpose of this study is to find out whether image similarity between celebrities and brands on social network service be a determinant to predict advertising effectiveness. To this end, an advertising effect prediction model for celebrity endorsed advertising was created and its validity was verified through a machine learning method which is a big data analysis technique. Firstly, the celebrity-brand image similarity, which was used as an independent variable, was quantified by the association network theory with social big data, and secondly a multiple regression model which used data representing advertising effects as a dependent variable was repeatedly conducted to generate an advertising effect prediction model. The accuracy of the prediction model was decided by comparing the prediction results with the survey outcomes. As for a result, it was proved that the validity of the predictive modeling of advertising effects was secured since the classification accuracy of 75%, which is a criterion for judging validity, was shown. This study suggested a new methodological alternative and direction for big data-based modeling research through celebrity-brand image similarity structure based on social network theory, and effect prediction modeling by machine learning.

• Management & Information Systems Review
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• v.32 no.3
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• pp.127-151
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• 2013

This research was to investigate the role of message regulatory focus on the advertising effect of multiple models. The multiple models are limited to two models in one advertisement in this research. This research investigated the hypothesis that multiple models, in terms of multiple source effect and social consensus, appears specifically to enhance the relationship of consumer and commercial models on the conditions of promotion-focused message which leads to the information processing of relational elaboration. This research applied a between-subjects factorial design targeting 2(the number of model: single model vs. multiple models) by 2(message regulatory focus: promotion-focused message vs. prevention-focused message). As a result, multiple models showed more positive ad attitude, brand attitude, and purchase intention than did single model. And promotion-focused message with multiple models showed more positive ad attitude, brand attitude, and purchase intention than did prevention-focused message with multiple models. Also there was an interaction effect between the number of model and the type of message regulatory focus. It wasn't fully supported that there is no difference of advertising effect between promotion-focused message and prevention-focused message on the condition of single model.

• The Journal of Bigdata
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• v.7 no.2
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• pp.31-41
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• 2022

Steel is one of the most fundamental components to mechanical industry. However, the quality of products are greatly impacted by the surface defects in the steel. Thus, researchers pay attention to the need for surface defects detector and the deep learning methods are the current trend of object detector. There are still limitations and rooms for improvements, for example, related works focus on developing the models but don't take into account real-time application with practical implication on industrial settings. In this paper, a real-time application of steel surface defects detection based on YOLOv4 is proposed. Firstly, as the aim of this work to deploying model on real-time application, we studied related works on this field, particularly focusing on one-stage detector and YOLO algorithm, which is one of the most famous algorithm for real-time object detectors. Secondly, using pre-trained Yolov4-Darknet platform models and transfer learning, we trained and test on the hot rolled steel defects open-source dataset NEU-DET. In our study, we applied our application with 4 types of typical defects of a steel surface, namely patches, pitted surface, inclusion and scratches. Thirdly, we evaluated YOLOv4 trained model real-time performance to deploying our system with accuracy of 87.1 % mAP@0.5 and over 60 fps with GPU processing.

• 한국해양학회지
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• v.27 no.4
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• pp.332-341
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• 1992

한반도 주변의 정규적인 해양관측 효시는 1910년대로부터 시작된다. 1915년 6월부 터 원산, 부산 등 12개 항구의 중앙부에서 10일 간격으로 해양관측이 시작되었으며, 1916년 7월부터는 거문도 해야 어청도 등 10개 등대관측소에서 연안정지관측을 역일로 시작했고, 1917년 5월부터 정선해양관측이 이루어 졌는데 이들은 총독부 수산과에서 수산시험조사사업의 일환으로 수행되었고 1921년부터 총독부수산 시험장에서 이들을 더욱 발전다. 1930년대에는 정선해양관측이 매월 초순 각 도별로 수항되어 가장 훌륭 한 관측결과가 생산되었다. 1961년부터는 항구관측을 폐지하고 연안정지관측과 정선해 양관측만 이루어 지고 있으며 해양조사선을 20여개로 수정하여 2개월마다 조사에 임하 고 있다. 이들은 기본적으로 해양생물자원의 경제적 획득에 목적을 두고 있으나 자료 를 널리 공개하여 전세계의 어느 해양연구자들도 이용할 수 있도록 하고 있다. 1960년 부터는 우리 나라 연안의 조석관측도 연속적으로 수행되고 있다. 1990년 현재 21개 정 선해양관측자료와 42개의 연안정지관측자료, 매일의 표면수온분포도 21개의 평균해면 자료가 공개적으로 이용가능하다. 앞으로 유명한 연구소일수록 해양관측자료를 공개하 여 공동이용할 수 있어야 하며 해양관측의 활성화를 위해 첫째 해양관측기구를 소모품 으로 취급할 것이 요구되고, 둘째 관측선 요원의 정당한 처우가 이루어져야 하며, 셋 째 세계적인 해양조사 사업은 국가기관에서 더욱 성실히 수행될 수 있도록 여건을 조 성할 필요가 있다. 과거에는 어업활동을 위해 해양조사가 현재에는 기후변동연구에 중 요한 자료로도 이용되고 있으므로 우리는 우리주변의 해양관측을 미래학문의 기초자료 로 끊임없이 수행해야 할 중차대한 임무를 지니고 있다.는 대체로 Weddell Sea쪽에서 남동쪽으로 가면서 증가하며, 영 양염 농도가 낮은 얼음 녹은 물의 유입이 얼룩소 a의 농도를 감소시키는 것으로 사료 된다.되어, 경기만에서 출현하는 식물플랑크톤이 서해 중동부 연안수역에서 출현하는 식물 플랑크톤보다 상대적으로 낮은 광에 적응되어 있었다. the most important in the global optimum analysis because small variation of it results in the large change of the objective function, the sum of squares of deviations of the observed and computed groundwater levels. 본 논문에서는 가파른 산사면에서 산사태의 발생을 예측하기 위한 수문학적 인 지하수 흐름 모델을 개발하였다. 이 모델은 물리적인 개념에 기본하였으며, Lumped-parameter를 이용하였다. 개발된 지하수 흐름 모델은 두 모델을 조합하여 구성되어 있으며, 비포화대 흐름을 위해서는 수정된 abcd 모델을, 포화대 흐름에 대해서는 시간 지체 효과를 고려할 수 있는 선형 저수지 모델을 이용하였다. 지하수 흐름 모델은 토층의 두께, 산사면의 경사각, 포화투수계수, 잠재 증발산 량과 같은 불확실한 상수들과 a, b, c, 그리고 K와 같은 자유모델변수들을 가진다. 자유모델변수들은 유입-유출 자료들로부터 평가할 수 있으며, 이를 위해서 본 논문에서는 Gauss-Newton 방법을 이용한 Bard 알고리즘을 사용하였다. 서울 구로구 시흥동 산사태 발생 지역의 산사면에 대하여 개발된 모델을 적용하여 예제 해석을 수행함으로써, 지하수 흐름 모델이 산사태 발생 예측을 위하여 이용할 수 있음을 입증하였다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.2
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• pp.33-41
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• 2001

Currently, the compression is one of the most important technology in multimedia society. Audio files arc rapidly propagated throughout internet Among them, the most famous one is MP-3(MPEC-1 Laver3) which can obtain CD tone from 128Kbps, but tone quality is abruptly down below 64Kbps. MPEC-II AAC(Advanccd Audio Coding) is not compatible with MPEG 1, but it has high compression of 1.4 times than MP 3, has max. 7.1 and 96KHz sampling rate. In this paper, we propose an algorithm that decreased the capacity of AAC encoding computation but increased the processing speed by optimizing psychoacoustic model which has enormous amount of computation in MPEG 2 AAC encoder. The optimized psychoacoustic model algorithm was implemented by C++ language. The experiment shows that the psychoacoustic model carries out FFT(Fast Fourier Transform) computation of 3048 point with 44.1 KHz sampling rate for SMR(Signal to Masking Ratio), and each entropy value is inputted to the subband filters for the control of encoder block. The proposed psychoacoustic model is operated with high speed because of optimization of unpredictable value. Also, when we transform unpredictable value into a tonality index, the speed of operation process is increased by a tonality index optimized in high frequency range.

• Aerospace Engineering and Technology
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• v.9 no.2
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• pp.14-21
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• 2010

Ion thruster is a kind of electrostatic thruster that use electrostatic field in order to accelerate ionized propellant. Ion thruster have characteristics of small thrust but very high specific impulse among the electric thrusters. High specific impulse can reduce propellant consumption significantly. So, ion thruster have advantage for long time and long distance mission. Recently, plans for space exploration is increasing gradually not only at traditional forward countries for space like USA, Russia and Europe, but also other countries like Japan, China and India. Exploration for superior planets and asteroids the propellant ratio can go up to about 99% when chemical propulsion is used as a cruising thruster. Therefore, latest space exploration vehicles use the ion thruster as main thruster for del-V burn and use monopropellant thrusters for attitude control. In this paper, the development process of preliminary ECR ion thruster and the ECR discharge test results will be presented.

• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.1-7
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• 2022

Strawberry is a stand-out cultivating fruit in Korea. The optimum production of strawberry is highly dependent on growing environment. Smart farm technology, and automatic monitoring and control system maintain a favorable environment for strawberry growth in greenhouses, as well as play an important role to improve production. Moreover, physiological parameters of strawberry plant and it is surrounding environment may allow to give an idea on production of strawberry. Therefore, this study intends to build a machine learning model to predict strawberry's yield, cultivated in greenhouse. The environmental parameter like as temperature, humidity and CO₂ and physiological parameters such as length of leaves, number of flowers and fruits and chlorophyll content of 'Seolhyang' (widely growing strawberry cultivar in Korea) were collected from three strawberry greenhouses located in Sacheon of Gyeongsangnam-do during the period of 2019-2020. A predictive model, Lasso regression was designed and validated through 5-fold cross-validation. The current study found that performance of the Lasso regression model is good to predict the number of flowers and fruits, when the MAPE value are 0.511 and 0.488, respectively during the model validation. Overall, the present study demonstrates that using AI based regression model may be convenient for farms and agricultural companies to predict yield of crops with fewer input attributes.