본 연구는 참가자 간 표상 유사성 분석(intersubject representation similarity analysis: IS-RSA)을 이용하여 3개의 선행연구에서 얻어진 데이터의 참가자 반응 일치성 패턴을 확인하고 각 실험의 정서 조건 간 차이가 있는지 살펴보 았다. 3개의 실험은 각각 ASMR 자극, 시각 및 청각 자극, 시계열적 정서 동영상 자극을 사용하였으며 각 실험의 조건에 맞게 정서 평정치와 생리측정치를 측정하였다. 참가자 간 표상 유사성 분석을 계산하기 위해서 각 실험에 있는 각 자극에 대한 참가자들의 측정치를 쌍별로 피어슨 상관계수를 구하였다. 실험의 조건 간 비교를 위해 분산분 석과 평균을 비교하였다. 연구 결과, ASMR과 시각 및 청각 데이터의 참가자 간 반응의 일치성은 시계열적 정서 동영상 참가자들 반응의 일치성에 비해 일관적이었다. ASMR 실험은 긍정 자극에서 참가자 간 반응의 일치성이 높았다. 청각 및 시각 실험은 높은 각성수준과 시각 자극에서 참가자들의 반응 일치성이 높았다. 본 연구 결과는 생리 적, 행동적 반응에 대한 측정치의 IS-RSA가 다차원적인 데이터의 정보를 요약하여 제시하며 이를 하나의 분석 데이 터로 변환 가능하다는 것을 확인하였다. 이를 통해, IS-RSA가 참가자들의 반응 일관성에 대한 전반적인 정보를 제시 할 수 있는 새로운 분석 방법으로의 가능성을 제시하였다.

본 연구에서는 코로나 이후 색조화장품 시장의 소비자들의 온라인 관심 정보에 대한 자료 수집 을 통하여 색조화장품 정보 검색의 특성과 텍스트 마이닝 분석 결과에 나타난 코로나 이후 색조화장품 시 장의 주요 관심정보들을 분석하고자 하였다. 실증분석에서는 “색조화장품” 이라는 단어를 포함하는 뉴스, 블로그, 카페, 웹페이지 등의 모든 문서들을 분석 대상으로 텍스트 마이닝을 수행하였다. 분석 결과 코로나 이후 색조화장품에 대한 온라인 정보 검색은 주로 구매 정보와 피부와 마스크 관련 화장법 등에 관한 정보 와 관심 브랜드와 행사 정보 등의 주요 토픽이 주를 이루고 있었다. 결과적으로 코로나 이후 색조화장품 구매자들은 적극적인 온라인 정보 검색을 통하여 제품 가치와 안전성, 가격 혜택, 매장 정보 등의 구매 정 보에 더욱 민감하게 될 것이므로 이에 대한 대응전략이 요구된다.

본 연구는 성인 흡연자의 심근경색증 조기 증상 인지 정도를 파악하고 인지와 관련된 요인을 분석하기 위해 진행되었다. 2021년 지역사회 건강조사 원시 자료를 활용한 서술적 조사연구로 조사 완료자 229,942명 중 본 연구에 해당하는 설문에 응답이 불충분한 대상자 18,343명을 제외한 210,899명을 연구대 상자로 선정하여 SAS 9.4 program을 이용하여 표본 분석, 빈도, 백분율 등의 기술통계, 카이 제곱 검정, 복합표본 로지스틱 회귀분석을 사용하였다. 나이, 혼인 여부, 교육수준, 금연계획 여부, 건강검진_암 검진 수검 여부, 당뇨병 진단 여부, 주관적 건강 수준, 사회 물리적 환경이 심근경색증 조기 증상 인지와 유의한 관련이 있었다. 연구 결과를 바탕으로 심근경색증 발생 위험이 높은 고위험 집단에 대한 적극적인 홍보 및 교육이 필요하다.

In this study, we proposed a simulator for the development of a digital multi-process welding machine and a welding process monitoring system. The simulator, which mimics the data generation process of the welding machine, is composed of process control circuit, peripheral device circuit, and wireless communication circuit. Utilizing this simulator, we aimed to develop a welding process monitoring system that can monitor the welding situations of four multi-process welding machines and three processes each, with data transmission through wireless communication. Through the operation of the proposed simulator, sequential digital processing of multi-process welding data and wireless communication were achieved. The welding process monitoring system enabled real-time monitoring and accumulation of the process data. The selection of upper and lower limits for process variables was carried out using a deep neural network based on allowable changes in bead shape, enabling the management of welding quality by applying a process control technique based on the trend of received data.

It is very important to measure and analyze various driving performance in the vehicle development stage. Particularly in racing vehicles, analysis of driving characteristics on various courses is very important, and data measurement and analysis technology using actual measurement equipment are widely used in racing strategies. In this paper, we present an analytical approach using vehicle acceleration, which is relatively easy to measure among various factors. Measured acceleration data is used to analyze optimal driving performance.

We performed a study to examine the association between diet quality and nonalcoholic fatty liver disease (NAFLD). Our study included 3,586 women aged 40-64 years who participated in the sixth Korea National Health and Nutrition Examination Survey. The study subjects were classified into the NAFLD group (n=816) and the normal group (n=2,770) using the hepatic steatosis index. The anthropometric indices, blood profiles, and dietary intake data of the subjects were obtained. The waist circumference, body mass index, and the serum levels of triglycerides, fasting blood sugar, HbA1c, and systolic and diastolic blood pressures were higher in the NAFLD compared to the normal groups (p<0.001, respectively). The intakes of protein (g/kg body weight, p<0.001), potassium (p<0.001), and vitamin A (p=0.006) were significantly lower in the NAFLD group. It was observed that the higher the total Korean Healthy Eating Index score, the lower the risk of NAFLD. A reverse relationship was shown between the NAFLD risk and the intakes of total fruits, total vegetables, vegetables excluding Kimchi and pickled vegetables, meat, fish, eggs and beans. Therefore, it is recommended that middleaged women in Korea increase their intakes of fruits, vegetables, and foods high in protein for the proper management of NAFLD.

We present a novel method that can enhance the detection success rate of interstellar objects. Interstellarobjects are objects that are not gravitationally bound to our solar system and thus are believed to haveoriginated from other planetary systems. Since the nding of two interstellar objects, 1l/`Oumuamua in2017 and 2l/Borisov in 2019, much attention has been paid to nding new interstellar objects. In thispaper, we propose the use of Heliospheric Imagers (HIs) for the survey of interstellar objects. In particular,we show HI data taken from Solar TErrestrial RElation Observatory/Sun Earth Connection Coronal andHeliospheric Investigation and demonstrate their ability to detect `Oumuamua-like interstellar objects. HIs are designed to monitor and study space weather by observing the solar wind traveling throughinterplanetary space. HIs provide the day-side observations and thus it can dramatically enlarge theobservable sky range when combined with the traditional night-side observations. In this paper, we rstreview previous methods for detecting interstellar objects and demonstrate that HIs can be used for thesurvey of interstellar objects.

군사학은 급변하는 안보환경과 국제정세의 변화, 4차산업혁명시대의 무기체계 발전과 저출산에 따른 병역제도 등의 사회적 관심이 증대되 고 있다. 따라서 본 연구는 빅데이터를 활용한 텍스트마이닝 기법으로 군사학의 학술연구 동향과 사회적 인식을 분석하여 시사점을 제시하는 데 있다. 연구 결과 학술연구 동향은 주변국 관계, 무기체계, 방위산업, 인공지능 등이 중점을 이루었지만, 사회적 인식은 대학교와 군사학과, 장교 등의 관심으로 차이점을 보였다. 군사학 발전을 위해 연구 중심의 역량과 환경을 구축하고, 융·복합적 연구와 지역사회와 연계한 산학협 력 체계구축 및 국민 참여를 통한 학술 세미나 및 통합연구 등이 요구 되었다.

PURPOSES : This paper proposes an artificial neural network (ANN)-based real-time traffic signal time design model using real-time field data available at intersections equipped with smart intersections. The proposed model generates suitable traffic signal timings for the next cycle, which are assumed to be near the optimal values based on a set of counted directional real-time traffic volumes. METHODS : A training dataset of optimal traffic signal timing data was prepared through the CORSIM Optimal Signal Timing program developed for this study to find the best signal timings, minimizing intersection control delays estimated with CORSIM and a heuristic searching method. The proposed traffic signal timing design model was developed using a training dataset and an ANN learning process. To determine the difference between the traditional pre-time model primarily used in practice and the proposed model, a comparison test was conducted with historical data obtained for a month at a specific intersection in Uiwang, Korea. RESULTS : The test results revealed that the proposed method could reduce control delays for most of the day compared to the existing methods, excluding the peak hour periods when control delays were similar. This is because existing methods focus only on peak times in practice. CONCLUSIONS : The results indicate that the proposed method enhances the performance of traffic signal systems because it rapidly provides alternatives for all-day cycle periods. This would also reduce the management cost (repeated field data collection) required to increase the performance to that level. A robust traffic-signal timing design model (e.g., ANN) is required to handle various combinations of directional demands.

PURPOSES : Most Red bus1) (metropolitan bus) routes to Seoul need to increase supply by increasing the number of buses and number of trips because of the high level of congestion in buses, which also accommodate standing passengers. Due to the recent Itaewon disaster, people have been banned from standing on Red buses due to concerns over the excessive use of public transportation, adding to the inconvenience of passengers, such as increased travel time. However, some routes incur a large deficit owing to excess vehicles and trips relative to the number of passengers, thereby increasing the financial burden of Gyeonggi. Therefore, in this study, a reasonable operation plan is required based on the demand on Red bus routes. METHODS : Using accurate data from smart cards and a Bus Management System, the model was applied to consider bus usage, bus arrival distribution, waiting time, and operating conditions, such as actual bus usage time and bus dispatch interval. RESULTS : As a result of applying the model, buses between 7:00 and 9:00 and 16:00 and 18:00 were very crowded because of standing passengers, and passenger inconvenience costs decreased because of the longer waiting times for bus stops in Seoul. Currently, there are 15 buses in operation for the red bus G8110. However, considering the annual transportation cost, transportation income, and support fund limit, up to 12 buses can be operated per day. The G8110 route was analyzed at 23.6 million won for passenger discomfort cost, as 15 buses operated 97 times per day on weekdays. However, when establishing optimal scheduling, 12 buses per day operated 75 times per day, with a 19.7 million won passenger discomfort cost. CONCLUSIONS : As all red buses run from the starting point, passengers at the bus stop wait for more than an hour before entering Seoul, and the passenger discomfort cost of using demand-responsive chartered buses decreases only when commuting from Jeongja Station and Namdaemun Tax Office stops. Currently, many people commuting from Gyeonggi-do to Seoul are experiencing significant inconvenience owing to the ban on standing in Red buses; a suitable level of input can be suggested for the input and expansion of chartered buses.

PURPOSES : The objective of this study is to develop the data driven pavement condition index by considering the traffic and climatic characteristics in Incheon city. METHODS : The Incheon pavement condition index (IPCI) was proposed using the weighted sum concept with standardization and coefficient of variation for measured pavement performance data, such as crack rate, rut depth, and International Roughness Index (IRI). A correlation study between the National Highway Pavement Condition Index (NHPCI) and Seoul Pavement Condition Index (SPI) was conducted to validate the accuracy of the IPCI. RESULTS : The equation for determining the IPCI was developed using standardization and the coefficient of variation for the crack rate, rut depth, and IRI collected in the field. It was found from the statistical analysis that the weight factors of the IPCI for the crack rate were twice as high as those for the rut depth and IRI. It was also observed that IPCI had a close correlation with the NHPCI and SPI, albeit with some degree of scattering. This correlation study between the NHPCI and SPI indicates that the existing pavement condition index does not consider the asymmetry of the original measured data. CONCLUSIONS : The proposed pavement condition provides an index value that considers the characteristics of the original raw data measured in the field. The developed pavement condition index is extensively used to determine the timing and method of pavement repair, and to establish pavement maintenance and rehabilitation strategies in Incheon.

PURPOSES : Due to the frequent occurrence of accidents on icy roads during nighttime, it would be advantageous to notify road managers and drivers about the most perilous areas. This would allow road managers to treat the icy roads with de-icing chemicals and enable drivers to be better prepared for potential hazards. Essential information about pavement temperature is required to identify icy spots on the road. METHODS : With the goal of estimating nighttime pavement temperature on the National Highways in Korea using atmospheric data, the current study investigated a widely recognized forecasting method known as deep neural network (DNN). To achieve this objective, the input data for the models were gathered from the weather agency's website. The dataset comprised of relative humidity, air temperature, dew point temperature, as well as the differences in air temperature and humidity between two consecutive days. RESULTS : In order to assess the effectiveness of the built DNN model, a comparison was made using baseline pavement temperature data gathered through an infrared-based pavement temperature sensor installed in a highway patrol car. The results indicated that the DNN model achieved a mean absolute error (MAE) of 0.42 and a root mean square error (RMSE) of 0.62. In comparison, a conventional regression model yielded an MAE of 2.07 and an RMSE of 2.64. Thus, the DNN model demonstrated superior performance in comparison to the conventional regression model. CONCLUSIONS : Considering the increasing focus on preventive maintenance, these newly developed prediction models can be implemented proactively as a preventive measure against icing. This proactive approach has the potential to significantly improve traffic safety on winter roads.

In today's fast-evolving technological environment, the growth of mobile devices has provided businesses with additional knowledge on how to effectively communicate with their targeted audience. As big data generated by mobile users has become a valuable source for improving communication, users’ location data has provided an additional parameter to take into account while trying to enhance the effectiveness of digital marketing campaigns. This paper presents a comparative study of the location-based marketing field and tools. It provides an overview of different exploitation methods of location-based mobile services and data for effective digital marketing campaigns and guidelines for future research.

This study empirically verified the endorsement effect of a restaurant platform as a reliable third-party organization on changes in customers’ attitudes toward restaurants by measuring big data-based consumption values using customers’ reviews and applying difference-in-differences. The results indicated that endorsement effects are effective when specific cues are provided and under unusual circumstances such as the pandemic.

This study explores the effectiveness of marketing data analytics learning and outcomes for marketing students in courses with data analysis components at a U.S. business school. The study considers various moderating factors, such as software adaptability, grades, class type, data interest, statistical analysis method, and perceived time- and cost-effectiveness. The findings have implications for marketing education in data analytics.

The wide application of digital media technology in fashion shows has become the epitome of the development and innovation of today's fashion industry, enabling designers to break through the constraints of time and space, changing the performance of today's fashion shows, and making them present unprecedented new features. With the development of information technology, the integration of emerging digital technology and the fashion industry is accelerating. So far, separate studies have been carried out in various academic fields on the combination of Metaverse and NFT, but the current status and nature of relevant research are still incomplete. Furthermore, the current research on virtual fashion shows and NFT in China's apparel industry is limited. The purpose of this study is to investigate the influence of digital fashion marketing stimulation on consumer brand attitudes using the stimulation-organ-response (SOR) framework model. By analyzing 77 cases of virtual fashion shows in China, this study obtained antecedent variables and designed a research model. An online sample of 300 Chinese Gen Z consumers was collected and analyzed using SPSS and FSQCA. This research hopes to provide valuable information for the sustainable development of China's fashion industry, and to help Chinese fashion brands confirm the future market development direction of Metaverse and NFT.

Webpage cookies collect and authorize access to users’ online footprints and regulate the data authorization for access, sharing, and usage. Data authorization, which is built based on, but exceeding cookies protocol, enables personalized recommendations under the framework of data-driven content-user matching in a way against customer privacy invasiveness and data breaches. However, gaps exist in how users’ desire for a personalized experience and the site’s perceived ethics contribute to the site-trust and cookies acceptance of categories at each type of site and how businesses’ reward incentives and cookie-based controls may intensify the willingness to contribute to the user data donation continuously.

This research focuses on analyzing the impact of a recommendation system on customer behavior in the e-commerce industry. The study examines the use of big data-driven product recommendations and tailored promotions to enhance customer engagement, conversion rates, and revenue generation. The importance of prioritizing customer engagement in the early stages of the purchasing process is emphasized, and key statistics related to customer behavior in e-commerce are presented. The objective of this research is to investigate the effectiveness of a recommendation system in influencing customer behavior and driving conversions in the e-commerce industry. The research design incorporates a case study analysis of a prominent marketplace in Indonesia. Data was collected from three automation trigger campaigns: browsing abandonment, wishlist/cart abandonment, and purchase reminders. The findings of the research indicate that a recommendation system based on big data has a significant impact on costumer behavior in the e-commerce industry. The research highlights the importance of prioritizing customer engagement and implementing effective recommendation systems to drive conversion rates and revenue in the e-commerce industry.

This study aims to investigate online commerce repertoire-based clusters and their characteristics with shopping values and commerce attributes. This study analyzes Nielsen panel log data that recorded nearly 6,000 panelists’ use of 48 major commerce websites and mobile applications. In addition, a survey was conducted with a sample of the panelists, which supplemented the behavioral data and provided cognitive and attitudinal data. Six commerce clusters were identified: “Social commerce centric,” “Secondhand centric,” “PC centric,” “scattered,” “Home-shopping centric,” and “Fashion centric". Also, “Hedonic” was statistically significant and “Quick delivery,” “Membership” are perceived to be effective. Also, there were discrepancies between the log and survey on usage. As online marketing and advertising driving conversion becomes critical, the understanding of online commerce repertoires and related consumer perceptions and characteristics should offer significant implications.

This study was to investigate the cultivation technique previously conducted cultivation research for the stable production of alfalfa and to present further research. The data used in the study were 270 alfalfa cultivation experimental data from 1983 to 2008, indicating the cultivation region, field type, variety, sowing, cutting frequency, fertilization, and dry matter yield (DMY). The average DMY of alfalfa in the Republic of Korea was 12,536 kg/ha, which differed greatly depending on the cultivated region. Most of the field type was cultivated in upland. In order to increase alfalfa production, it is necessary to use reclaimed and unused land, and research on the soil amendment matter to improve the soil condition is needed. Alfalfa varieties cultivated an amount of 53, but collected data no studies considered fall dormancy, the criteria for selecting alfalfa varieties, so further research is required. The fertilizer did not consider each component at various levels, and research is needed as the demand for fertilizer. In particular, research on potassium is needed considering the increase in alfalfa production. The alfalfa cutting frequency differed in the estimated pasture production period depending on the region, and the DMY tended to increase with increasing cutting frequency. This suggests that the alfalfa DMY can be increased according to the cutting frequency in the Republic of Korea, so research is needed to present the appropriate cutting frequency.