본 연구는 2020년 이후 한류에 대한 소비자의 인식을 살피고자 중국 내 한류에 대 한 인식과 한국 내 한류 인식을 파악하여 양국의 현황을 분석하는 것을 목적으로 한 다. 이를 위해 텍스톰(TEXTOM)을 활용하여 2020년∼2023년 동안의 주요 포털사이 트 및 SNS에서 ‘한류’와 관련된 빅데이터를 수집하였다. 단어빈도분석, 감성분석, 네 트워크 시각화, 구조적 등위성 분석을 수행하여 유의한 정보를 추출하였다. 연구결과 팬데믹 이후 양국의 영상콘텐츠에 대한 높아진 관심은 공통적인 현상이었으나, 한류 에 대한 긍‧부정 감성, 담론에 있어서는 새롭고 유의미한 차이점이 도출되어 이를 기반으로 학문적, 실무적 시사점을 제시하였다. 본 연구가 신(新)한류 시기에 K-콘텐 츠의 한·중 인식파악을 위한 유용한 자료로 활용될 수 있길 바란다.

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

The increasing number of technology transfers from public research institutes in Korea has led to a growing demand for patent recommendation platforms for SMEs. This is because selecting the right technology for commercialization is a critical factor in business success. This study developed a patent recommendation system that uses technology transfer data from the past 10 years to recommend patents that are suitable for SMEs. The system was developed in three stages. First, an item-based collaborative filtering system was developed to recommend patents based on the similarities between the patents that SMEs have previously transferred. Next, a content-based recommendation system based on TF-IDF was developed to analyze patent names and recommend patents with high similarity. Finally, a hybrid system was developed that combines the strengths of both recommendation systems. The experimental results showed that the hybrid system was able to recommend patents that were both similar and relevant to the SMEs' interests. This suggests that the system can be a valuable tool for SMEs that are looking to acquire new technologies.

Recently, the importance of impact-based forecasting has increased along with the socio-economic impact of severe weather have emerged. As news articles contain unconstructed information closely related to the people’s life, this study developed and evaluated a binary classification algorithm about snowfall damage information by using media articles text mining. We collected news articles during 2009 to 2021 which containing ‘heavy snow’ in its body context and labelled whether each article correspond to specific damage fields such as car accident. To develop a classifier, we proposed a probability-based classifier based on the ratio of the two conditional probabilities, which is defined as I/O Ratio in this study. During the construction process, we also adopted the n-gram approach to consider contextual meaning of each keyword. The accuracy of the classifier was 75%, supporting the possibility of application of news big data to the impact-based forecasting. We expect the performance of the classifier will be improve in the further research as the various training data is accumulated. The result of this study can be readily expanded by applying the same methodology to other disasters in the future. Furthermore, the result of this study can reduce social and economic damage of high impact weather by supporting the establishment of an integrated meteorological decision support system.

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.

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 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 : This study proposes an index for analyzing mobility based on smartcard and taxi data to evaluate imbalances in public transit. METHODS : The proposed mobility index is calculated based on the difference between the mobility indexes of public transit and taxis using the variables of the in-vehicle time, waiting time, and driving ratio. For a more detailed analysis, the distances are divided into short distances, medium distances, and long distances. RESULTS : Public transit mobility indexes are generally evenly distributed, but the taxi mobility indexes are located in the largest legend. When comparing the respective mobilities of public transit and taxis, many areas with a high mobility of taxis (similar to the distribution) exist, especially in the outskirts such as Dobong-gu, Nowon-gu, Gangdong-gu, Guro-gu, Geumcheon-gu, and Eunpyeong-gu. On average, the mobility of public transit according to the distance is smaller in a short distance and higher in a long distance. CONCLUSIONS : The results demonstrate the use of the proposed index for analyzing the basic statuses of complementary indexes for evaluating public transit imbalances. In the future, more detailed results (including socioeconomic variables corresponding to the grid areas) should be studied to identify the impacts of the mobility index.

본 연구는 빅데이터를 통해 남북통일에 관한 한국 사회의 인식을 분석 하고 그를 통한 함의를 도출하여 미래 한반도 통일에 관한 시사점을 얻 고자 하였다. 이를 위해 2003년부터 2022년 5월까지, 역대 4개 정부를 분석대상 기간으로 구분 설정하고 포털 및 언론보도 빅데이터 총 115,975건의 데이터를 수집하여 텍스트마이닝과 사회연결망 분석을 수행 하였다. 연구 결과, 정부 시기별 통일에 관한 사회적 인식의 특성을 확인 하였으며 외교, 정치, 군사, 경제, 사회·문화 측면의 공통 키워드를 도출 하여 그에 따른 한반도 통일에 관한 발전적 방향을 제언하였다.