PURPOSES : The purpose of this study was to evaluate the common performance of asphalt pavements, determine the timing of preventive maintenance, and determine the optimal timing of application of the preventive maintenance methods by analyzing PMS data. METHODS : Using PMS data on asphalt pavement performance on highways, we derived the major damage factors and evaluated them according to the public period and traffic level. Among the factors evaluated, we determined those that could be improved by preventive maintenance, calculated the amount of change annually, and derived the timing of the application of the preventive maintenance method through correlation analysis. RESULTS : Among highway PMS data factors, crack variation was found to affect preventive maintenance, which increased rapidly after five years of performance. Traffic analysis showed that changes increased rapidly in the fifth, sixth, and seventh years when AADT exceeded 20,000, exceeded 10,000, and was under 10,000, respectively. Analysis of the amount of crack variation according to the pavement type showed that crack variation increased rapidly in the overlay section compared to the general AP section. CONCLUSIONS : Crack variation is the performance factor that was expected to be effective in preventive maintenance, and the PMS data showed that the initial application time of the preventive maintenance method varied by one year, depending on the traffic volume.

The construction industry stands out for its higher incidence of accidents in comparison to other sectors. A causal analysis of the accidents is necessary for effective prevention. In this study, we propose a data-driven causal analysis to find significant factors of fatal construction accidents. We collected 14,318 cases of structured and text data of construction accidents from the Construction Safety Management Integrated Information (CSI). For the variables in the collected dataset, we first analyze their patterns and correlations with fatal construction accidents by statistical analysis. In addition, machine learning algorithms are employed to develop a classification model for fatal accidents. The integration of SHAP (SHapley Additive exPlanations) allows for the identification of root causes driving fatal incidents. As a result, the outcome reveals the significant factors and keywords wielding notable influence over fatal accidents within construction contexts.

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.

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.

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.

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.

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.

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.

Currently, the development of evaluation technology for vibration and shock loads transmitted to spent nuclear fuel and structural integrity of spent nuclear fuel under normal conditions of transport is progressing in Korea by the present authors. Road transportation tests using surrogate spent nuclear fuel were performed in September, 2020 using a test model of KORAD-21 transportation cask and sea transportation tests were conducted from September 30 to October 4, 2021. Finally, the shake table tests and rolling test were conducted from October 31 to November 2, 2022. The shake table test was performed with the input load produced conservatively from the data obtained from the road and sea transportation tests. The test input was produced based on the power spectral densities and shock response spectrums from the transportation tests. In addition to the test inputs from the road and sea tests, sine sweep input and half sine input were used to verify the vibration characteristics of assemblies under boundary conditions during normal conditions of transport. Because the input load of the shake table test was produced conservatively, a slightly larger strain than the strain value measured in road and sea transportation tests was measured from the shake table tests. In the case of the sea test, it is considered that the process of enveloping the data in the 20 to 80 Hz range generated by the engine propeller system was performed excessively conservatively. As a result of analyzing the test results for the difference in boundary conditions, it was confirmed that the test conditions of loading the basket generated a relatively large strain compared to the conditions of loading the disk assembly for the same input load. Therefore, it is concluded that a transportation cask having a structure in which a basket and a disk are separated, such as KORAD-21, is more advantageous in terms of vibration shock load characteristics under normal conditions of transport than a transportation cask having an integral internal structure in which a basket and a disk are a single unit. However, this effect will be insignificant because the load itself transmitted to the disk assembly is very small.