Abstract Handling imbalanced datasets in binary classification, especially in employment big data, is challenging. Traditional methods like oversampling and undersampling have limitations. This paper integrates TabNet and Generative Adversarial Networks (GANs) to address class imbalance. The generator creates synthetic samples for the minority class, and the discriminator, using TabNet, ensures authenticity. Evaluations on benchmark datasets show significant improvements in accuracy, precision, recall, and F1-score for the minority class, outperforming traditional methods. This integration offers a robust solution for imbalanced datasets in employment big data, leading to fairer and more effective predictive models.

As a safety device, a rupture disc are used to control pressure to minimize the explosion risk once the internal pressure of high pressure equipment exceeds a critical level. In this paper, optimization method was developed to secure optimal design of domed Rupture disks. The parameter analysis was performed through design of experiment to parameter of Rupture disk made of AISI 316.The Diameter, Thickness and Hight of Rupture disk were selected as design parameters for design parameter analysis. The results of parameter analysis revealed that the Diameter, thickness and hight were burst pressure-sensitive design parameters. Based on the valid performance factors, a regression equation to predict its performance was deducted and using the equation, an optimal design. And a sample model was fabricated, followed by burst pressure testing, after optimal design and analytical verification. In this research, it is verified that the optimal design method and the credibility of the analysis of this study is deemed very high. Furthermore, utilizing this mechanism would inspect the effect of the design parameter performance and increase the credibility and efficiency of a design.

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.

In the automobile manufacturing industry, lightweight design is one of the essential challenges to be solved fundamentally. The vehicle wheels are classified as safety related components as the main substructure of the vehicle. In this study, we illustrate a technique for selecting the appropriate number of spokes. Based on the basic model of the selected number of spokes, we propose a method to maintain stiffness and design lightweight using topology optimization software. Based on the basic model of the selected number of spokes, it was redesigned to be lightweight while maintaining stiffness by utilizing topology optimization software. By comparing and reviewing the structural analysis results of the basic model and the redesigned model, a design technique that can maintain structural safety and reduce wheel mass was proposed.

도서관은 문화 공간으로서 누구나 이용할 수 있고 다양한 분야의 지식과 정보를 제공하여 삶의 질을 높이는 중요한 사회기반 시설 중 하나이다. 현재 한국의 도서관 수는 수요에 비해 공급이 부족한 상황이며, 이를 해결하기 위해 일부 지자체는 차량을 수단으로 이동형 도서 서비스를 제공하는 분관 형태의 이동 도서관을 운영하고 있다. 주로 도서관을 이용하기 어려운 사람들을 대상으로 순회하며 도서관 서비스를 제공하지만, 비효율적인 운행 노선과 균일하지 않은 서비스로 실질적인 효과를 발휘하지 못하고 있다. 따라서 본 연구에서는 성남시 새마을 이동 도서관을 대상으로 순회 경로 현황을 파악하고, 최소 이동 거리로 개선된 이동 도서관 노선을 제시하고자 한다. 더욱 효율적인 운영을 위해 서비스 권역을 나누고, 시간 제약을 결합한 차량경로설정 문제를 사용하여 도서 서비스의 이용 격차를 줄인 새로운 운행 노선을 구축하였다. 본 연구는 이동 도서관의 효과적인 노선 운영에 대한 기초적인 자료로 활용될 수 있다는 점에서 의의가 있다. 향후 이동 도서관 뿐만 아니라 이동형 공공 서비스를 위한 유용하고 현실적인 가이드라인으로 활용될 수 있을 것이다.

PURPOSES : To efficiently manage pavements, a systematic pavement management system must be established based on regional characteristics. Suppose that the future conditions of a pavement section can be predicted based on data obtained at present. In this case, a more reasonable road maintenance strategy should be established. Hence, a prediction model of the annual surface distress (SD) change for national highway pavements in Gangwon-do, Korea is developed based on influencing factors. METHODS : To develop the model, pavement performance data and influencing factors were obtained. Exploratory data analysis was performed to analyze the data acquired, and the results show that the data were preprocessed. The variables used for model development were selected via correlation analysis, where variables such as surface distress, international roughness index, daily temperature range, and heat wave days were used. Best subset regression was performed, where the candidate model was selected from all possible subsets based on certain criteria. The final model was selected based on an algorithm developed for rational model selection. The sensitivity of the annual SD change was analyzed based on the variables of the final model. RESULTS : The result of the sensitivity analysis shows that the annual SD change is affected by the variables in the following order: surface distress ˃ heat wave days ˃ daily temperature range ˃ international roughness index. CONCLUSIONS : An annual SD change prediction model is developed by considering the present performance, traffic volume, and climatic conditions. The model can facilitate the establishment of a reasonable road maintenance strategy. The prediction accuracy can be improved by obtaining additional data, such as the construction quality, material properties, and pavement thickness.

Recently, considerable attention has been given to nickel-based superalloys used in additive manufacturing. However, additive manufacturing is limited by a slow build rate in obtaining optimal densities. In this study, optimal volumetric energy density (VED) was calculated using optimal process parameters of IN718 provided by additive manufacturing of laser powder-bed fusion. The laser power and scan speed were controlled using the same ratio to maintain the optimal VED and achieve a fast build rate. Cube samples were manufactured using seven process parameters, including an optimal process parameter. Analysis was conducted based on changes in density and melt-pool morphology. At a low laser power and scan speed, the energy applied to the powder bed was proportional to and not . At a high laser power and scan speed, a curved track was formed due to Plateau-Rayleigh instability. However, a wide melt-pool shape and continuous track were formed, which did not significantly affect the density. We were able to verify the validity of the VED formula and succeeded in achieving a 75% higher build rate than that of the optimal parameter, with a slight decrease in density and hardness.

Due to environmental pollution, regulations on fossil fuels are required. There is a movement for the regulations by using LNG fueled propulsion ships. LNG is an eco-friendly fuel that does not emit NOx or SOx during combustion, but its boiling point is -163°C. Under that condition, the use of metal is restricted, and IMO defined applicable materials through IGC code. Among the metals, 9% nickel steel is one of excellent mechanical properties such as yield strength and tensile strength in cryogenic condition. Thus 9% nickel steel is widely used in cryogenic storage containers for ships. In addition, laser welding, which minimizes thermoelastic distortion by applying a concentrated heat source to a narrow area for a short period of time, is in the spotlight. So, this study is a basic research to predict and respond to thermal distortion during laser welding. Secondary version of the representative heat source model was derived through the author's previous research with STS304L, and the heat source model was derived by applying the heat source model to 9% nickel steel in this study. 9% nickel steel is a material that is in high demand and is widely used in the manufacture of cryogenic containers, so this study is expected to be able to respond immediately to the field.

In this paper, to improve the optical quality of aspherical plastic lenses for mobile use, the optimal molding conditions that can minimize the phase difference are derived using injection molding simulation, design of experiments, and machine learning. First, factors affecting the phase difference were derived using the design of the experiment method, and a data set was created using the derived factors, followed by the machine learning process. After predicting the model trained using the generated training data as test data and verifying it with the performance evaluation index, the model with the best predictive performance was the random forest model. Therefore, to derive the optimal molding conditions, random forests were used to predict 10,000 random pieces of data. As a result of applying the derived optimal molding conditions to the injection molding simulation, the phase difference of the lens could be reduced by 8.2%.

Most of automobile steering parts are manufactured through the multi-stage cold forging process using round-bar drawn materials. The same process is applied to the ball stud parts of the outer ball joint, and various research activities are being carried out to reduce the extreme manufacturing cost in order to survive in the limitless competition. In this paper, we present a quantitative prediction method for the limiting life of the die as a method for cost reduction in the multi-stage cold forging process. The load on the die was minimized by distributing the forming load based on process optimization through finite element analysis. In addition, based on the quantitative prediction algorithm of the limiting life of the die, the application of the split die and the optimization of the phosphate treatment of the material surface are presented as a conclusion as a method to improve the limiting life of the die.