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.

이 연구는 지난 1년간 지속되었던 러시아-우크라이나 전쟁과 관련된 국내 언론매체의 전쟁 관련 보도를 거시적으로 살펴보는 것을 주요 목적 으로 하였다. 본 논문은 우크라이나 전쟁 관련 연구사례가 우리나라의 안보에 함의를 제공해 줄 수 있는 사안이라고 진단하고 있다. 따라서 해 당 사례의 언론 보도가 매체의 정파성에 따라 다르게 보도되는지 혹은 정파성과 상관없이 동일하게 보도되는지 살펴보고자 한다. 보수신문과 진보 신문을 각각 대표하는 조선일보와 한겨레신문의 러시아-우크라이나 전쟁 관련 기사를 수집 및 분석하였다. 본 연구는 빅데이터 내용분석을 통해 양적 분석을 시도하였으며, 결과를 도출하여 비교고찰을 시도했으 나 분석 결과 신문의 정파성에 따른 보도 행태에 유의미한 차이점을 발 견할 수 없었다. 이는 한국 언론이 우크라이나 전쟁에 대한 단편적인 정 보 전달을 중심으로 보도하는 것으로 해석된다. 연구 결과 향후 뉴스를 공급하는 전달자의 정보의 질과 뉴스를 공급받는 수용자의 정보 해석력 에 대한 현주소를 점검해볼 필요가 있다. 그리고 향후 연구에서 좀 더 다른 지역의 사례를 포함해 비교하는 것도 도움이 될 것으로 판단된다.

Machine learning-based data analysis approaches have been employed to overcome the limitations in accurately analyzing data and to predict the results of the design of Nb-based superalloys. In this study, a database containing the composition of the alloying elements and their room-temperature tensile strengths was prepared based on a previous study. After computing the correlation between the tensile strength at room temperature and the composition, a material science analysis was conducted on the elements with high correlation coefficients. These alloying elements were found to have a significant effect on the variation in the tensile strength of Nb-based alloys at room temperature. Through this process, a model was derived to predict the properties using four machine learning algorithms. The Bayesian ridge regression algorithm proved to be the optimal model when Y, Sc, W, Cr, Mo, Sn, and Ti were used as input features. This study demonstrates the successful application of machine learning techniques to effectively analyze data and predict outcomes, thereby providing valuable insights into the design of Nb-based superalloys.

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

PURPOSES : This study aims to suggest how to utilize "standby data" of shared mobility that does not contain personal information and examine whether "standby data" can derive existing shared mobility operation analysis items similarly. METHODS : An existing Personal Mobility (PM) traffic pattern analysis was performed by identifying the user (User ID) and the user's route in a time frame. In this study, the PM traffic pattern analysis focuses on a vehicle (ID of the standby vehicle) and its standby location. We examined whether the items derived from the User ID-based traffic pattern analysis could also be derived from the standby Vehicle ID-based analysis. RESULTS : The analysis showed that all five items (traffic volume by time slot, peak time, average travel time, average travel distance, and average travel speed) of the existing User ID-based PM travel analysis result could be derived similarly using the standby Vehicle ID-based PM traffic analysis. However, the disadvantage is that the average driving distance is calculated as a straight-line distance. It seems possible to overcome this limitation by correcting the average driving distance through linkage analysis with road network data. However, it is not possible to derive the instantaneous maximum speed or acceleration/deceleration. CONCLUSIONS : In an era in which various means of transportation are being introduced, data sharing is not preferred because of legal issues.Consequently, it is difficult to understand the use of new means of transportation and formulate new policies. To address this, data sharing can be active based on standby data that is not related to personal information.