PURPOSES : This study is to analyze the reduction effect on road pavement damage from the installation of weigh-in-motion systems used for overloaded vehicle enforcement, from the perspective of traffic assignment. METHODS : Fixed-demand multi-class traffic assignment was conducted by VISUM, a macroscopic traffic simulation software. We considered three vehicle classes and calculated the traffic load for each road link using the ESAL(Equivalent Single Wheel Load) factor, as proposed by ASHTTO(American Association of State Highway and Transportation Officials). We set up scenarios with weigh-in-motion installations in certain sections and observed how the traffic load changed before and after the installation of weigh-in-motion for each scenario. RESULTS : Three main trends were observed. Firstly, at points where weigh-in-motion systems were installed, traffic load significantly decreased even with the influx of cars and trucks following the restriction of overloaded trucks, highlighting the significant influence of overweight vehicles on the traffic load. Secondly, even when overweight vehicles detoured, there was no significant change in the overall network's traffic load. Lastly, the detour of overweight vehicles led to an increase in the total driving distance and time for all vehicles. CONCLUSIONS : Installing weigh-in-motion systems in sections with a lower structure number, which indicates thinner road pavement, can prevent damage in those specific areas without affecting the entire road network.

Maintaining sea superiority through successful mission accomplishments of warships is being proved to be an important factor of winning a war, as in the Ukraine-Russia war. in order to ensure the ability of a warship to perform its duties, the survivability of the warship must be strengthened. In particular, among the survivability factors, vulnerability is closely related to a damage assessment, and these vulnerability data are used as basic data to measure the mission capability. The warship's mission capability is usually measured using a wargame model, but only the operational effects of a macroscopic view are measured with a theater level resolution. In order to analyze the effectiveness and efficiency of a weapon system in the context of advanced weapon systems and equipments, a warship's mission capability must be measured at the engagement level resolution. To this end, not the relationship between the displacement tonnage and the weight of warheads applied in the theater level model, but an engagement level resolution vulnerability assessment method that can specify physical and functional damage at the hit position should be applied. This study proposes a method of measuring a warship’s mission capability by applying the warship vulnerability assessment method to the naval engagement level analysis model. The result can be used as basic data in developing engagement algorithms for effective and efficient operation tactics to be implemented from a single unit weapon system to multiple warships.

With the recent surge in YouTube usage, there has been a proliferation of user-generated videos where individuals evaluate cosmetics. Consequently, many companies are increasingly utilizing evaluation videos for their product marketing and market research. However, a notable drawback is the manual classification of these product review videos incurring significant costs and time. Therefore, this paper proposes a deep learning-based cosmetics search algorithm to automate this task. The algorithm consists of two networks: One for detecting candidates in images using shape features such as circles, rectangles, etc and Another for filtering and categorizing these candidates. The reason for choosing a Two-Stage architecture over One-Stage is that, in videos containing background scenes, it is more robust to first detect cosmetic candidates before classifying them as specific objects. Although Two-Stage structures are generally known to outperform One-Stage structures in terms of model architecture, this study opts for Two-Stage to address issues related to the acquisition of training and validation data that arise when using One-Stage. Acquiring data for the algorithm that detects cosmetic candidates based on shape and the algorithm that classifies candidates into specific objects is cost-effective, ensuring the overall robustness of the algorithm.

행복은 개인들이 추구하는 삶의 가장 중요한 목적이라고 할 수 있다. 본 연구에서는 행복 증진을 위해서 개인들이 스스로 자신의 웰니스를 점검하고 관리하는 것이 필요하다고 보고, 셀프 라이프코칭 앱의 웰니스 증진 효과에 대한 예비 연구를 진행하였다. 이를 위해, 총 36명의 참여자를 실험집단(1·2)과 통제집단에 무선배정하고, 실험집단에는 연구용 앱을 활용해 4주간 셀프 라이프코칭을 수행하도록 하였다. 실험처치 중 주간 피드백을 실험집단1에는 제공하 고, 실험집단2에는 제공하지 않았다. 종속변인으로 노동자를 위한 웰니스 지수(WIW)와 한국형 웰니스 척도(KWS) 를 사용하였다. 실험결과, 사전, 사후, 추후의 시점과 집단 간의 상호작용 효과는 통계적으로 유의미하지 않았으나, 효과의 경향성을 확인하였다. 주간 피드백 유무에 따른 실험처치 효과 차이는 확인되지 않았다. 시간에 따른 웰니스 증진 효과가 통계적으로 유의미했으며, 실험처치가 웰니스에 미치는 효과가 선형적임을 확인하였다. 참여의견 조사 결과, 참여자의 84.2%가 이번 활동이 도움이 된다고 하였으며, 78.9%가 상용화된 앱이 있다면 1주 또는 2주에 한번 사용하겠다고 응답하였다. 본 연구는 누구나 스스로 자신의 웰니스 증진에 활용할 수 있는 방법론을 제시하였으며, 그 효과를 일정 부분 확인하였다는 점에서 의의가 있다.

Recently, in the case of the root industry, although it is a basic industry that forms the basis of manufacturing competitiveness, there continues to be a shortage of manpower due to reasons such as dangerous working environments, industrial economic difficulties, and low wage systems. In addition, the demand for automation of production lines using robots is increasing due to a shrinking labor market due to a decrease in the working population due to aging, higher wages, shorter working hours, and limitations of foreign workers. In this study, a system was developed to automate the injection molding process for producing ball valves for automobiles by applying robot system. The applied process flow consists of alignment and insertion of insert parts, and removal, transfer, and loading of the product after injection molding, which is currently performed manually. Through the application of the developed robot automation system, the cycle time was improved by more than 30% and the defect rate was reduced by more than 70%.