제올라이트, 특히 ZSM-5는 독특한 구조와 분자 체 특성으로 인해 산업적으로 매우 유용하며, 우수한 가스 분리 및 투과 증발 성능으로 높은 평가를 받고 있다. 그러나 ZSM-5 막의 제조 공정을 일관되게 재현하는 것은 여전히 도전 과제 로 남아 있다. 본 연구는 수열합성 조건(합성 시간: 24~72 h, 온도: 180~220°C)을 제어하고, 다양한 알루미나 지지체 비교하 며, 수열 처리 시 유기 구조유도체의 영향 분석을 통해 ZSM-5 막 제조의 신뢰성 향상을 목표로 하였다. 연구 결과, 합성 온 도 및 시간의 변화는 막 두께나 결정 크기에 큰 영향을 미치지 않았으나, 180°C에서 48 h 합성 조건에서 가장 우수한 가스 투과 성능이 나타났다. 다양한 알루미나 지지체 중에서는 N5 α-알루미나 모세관 지지체가 가장 높은 투과도를 나타내었다. 또한, 유기 구조유도체인 테트라프로필 암모늄 브로마이드(tetrapropylammonium bromide, TPABr)의 존재는 합성의 신뢰성에 상당한 영향을 미치는 것으로 확인되었다. 가스 투과 성능 평가 결과, 본 ZSM-5 막은 SF₆에 비해 N2 및 CO2에 대해 선택적 인 투과 특성을 보였으며, TPABr을 사용하여 합성한 막은 CO2/N2 선택도(α)가 약 4.6으로 나타났다.

Humans have the ability to perceive an object’s material and properties instantaneously, and use this information to prepare for future actions. Material perception is not only an important factor for humans but also for artificial intelligence robots that are being developed. In addition, material perception is one of the important design requirements in selecting materials suitable for the products desired by consumers and pursued by designers. Because it is impossible to perform material perception using an exact formula, it is determined from tendencies that are identified in surveys. In this study, surveys with a binary selection were conducted, presenting participants with pairs of bipolar adjectives and asking them to choose one of two. After multiple surveys were conducted all the data were merged. Before merging the data, to ensure the reliability of the data homogeneity and correlation were tested using hierarchical clustering, correlation coefficient, and k-means cluster analysis. Afterwards, the merged data was used to analyze universal and comparable perceptual qualities of various material classes using relative frequency and hierarchical cluster analysis.

The 81mm mortar barrel is a precision component requiring exact internal diameter measurements, which poses challenges in both machining and inspection using conventional equipment. This study addresses issues related to measurement variability and reprocessing due to inconsistencies in bore measurements along different barrel positions. Using Gage R&R analysis, we identified the limitations of the existing measurement system, particularly its inability to provide consistent and detailed readings along the full barrel length. To resolve these challenges, a revised measuring system was developed and compared with the existing system, showing enhanced accuracy and reduced errors. The improved system was subsequently applied to the barrel machining process, resulting in quality improvements in the final product.

It is considered that a skincare shop is one of important places for skincare and beauty care. Due to increasing daily stress and environmental pollution recently, healthy skin is getting important and more people are visiting skincare shops. This study investigated the effects of laporte of skincare shop workers on customer reliability and satisfaction with a focus on women in twenties and thirties, and those of reliability on customer satisfaction, aiming at providing basic information for active management of skincare shops. For the purposes, this study used a questionnaire to collect the data and using SPSS 25.0. conducted a frequency test, an exploratory factor analysis, a descriptive statistical analysis, a correlation analysis and a reliability test to analyze the data collected. As a result of the study, it was discovered that laporte of skincare shop workers had a significant effect on customer reliability and satisfaction and reliability had a significant effect on customer satisfaction. It suggested that relationship between skincare shop workers and customers is very important for skincare shop management.

The design variables and material properties as well as the external loads concerned with structural engineering are used to be deterministic in optimization process. These values, however, have variability from expected performance. Therefore, deterministic optimum designs that are obtained without taking these uncertainty into account could lead to unreliable designs, which necessitates the Reliability-Based Design Optimization(RBDO). RBDO involves an evaluation of probabilistic constraints which constitutes another optimization procedure. So, an expensive computational cost is required. Therefore, how to decrease the computational cost has been an important challenge in the RBDO research field. Approximation models, response surface model and Kriging model, are employed to improve an efficiency of the RBDO.

본 논문에서는 신뢰성 기반 최적설계(RBDO)에서 성능함수의 비선형성을 고려한 효율적인 차원감소법(DRM)을 제안한다. 차원감 소법은 적분직교점과 가중치를 사용하여 1차 신뢰도법(FORM) 보다 더 정확하게 신뢰도를 평가하는 반면 성능함수를 추가로 해석해 야하기 때문에 적분직교점의 개수가 증가하면 효율성이 저해된다. 본 논문에서는 신뢰성 기반 최적설계에서 성능함수의 비선형도를 평가하고, 비선형도에 따라 적분직교점의 수를 결정하는 기준을 제안한다. 이를 통해 신뢰성 기반 최적설계가 진행될 때 반복마다 적 분직교점의 수를 조절하여 차원감소법의 정확도는 유지하면서 계산의 효율성은 개선하는 방안을 제안한다. 성능함수의 비선형도 평 가는 최대가능목표점(MPTP) 탐색에 사용한 벡터 사이의 각도를 통해 이루어지며, 수치 테스트를 통해 비선형도에 따른 적절한 적분 직교점의 수를 도출하였다. 2차원 수치예제를 통해 개발된 방법이 차원감소법이나 몬테카를로 시뮬레이션(MCS)의 정확도는 유지하 면서 효율성이 향상된다는 것을 확인하였다.

PURPOSES : We propose a framework to evaluate the reliability of integrating homogeneous or heterogeneous mobility data to produce the various data required for greenhouse gas emission estimation. METHODS : The mobility data used in the framework were collected at a fixed time from a specific point and were based on raster data. In general, the traffic volume for all traffic measurement points over 24 h can be considered raster data. In the future, the proposed framework can be applied to specific road points or road sections, depending on the presence or absence of raster data. RESULTS : The activity data required to calculate greenhouse gas emissions were derived from the mobility data analysis. With recent developments in information, communication, and artificial intelligence technologies, mobility data collected from different sources with the same collection purpose can be integrated to increase the reliability and accuracy of previously unknown or inaccurate information. CONCLUSIONS : This study will help assess the reliability of mobility data fusion as it is collected on the road, and will ultimately lead to more accurate estimates of greenhouse gas emissions.

Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.

In the semiconductor manufacturing clean room, contamination that directly affects process yield is managed through the operation of a monitoring system that measures molecular contamination in the air. In this study, I presented the component inspection method, test conditions, and judgment criteria through the life test of the solenoid valve that will be applied to the sampling module of the AMC Monitoring System.

The carbon concentration in the carburized steels was measured by electron probe microanalysis (EPMA) for a range of soluted carbon content in austenite from 0.1 to 1.2 wt%. This study demonstrates the problems in carbon quantitative analysis using the existing calibration curve derived from pure iron (0.008 wt%C) and graphite (99.98 wt%C) as standard specimens. In order to derive an improved calibration curve, carbon homogenization treatment was performed to produce a uniform Kα intensity in selected standard samples (AISI 8620, AISI 4140, AISI 1065, AISI 52100 steel). The trend of detection intensity was identified according to the analysis condition, such as accelerating voltage (10, 15, 30 keV), and beam current (20, 50 nA). The appropriate analysis conditions (15 keV, 20 nA) were derived. When the carbon concentration depth profile of the carburized specimen was measured for a short carburizing time using the improved calibration curve, it proved to be a more reliable and accurate analysis method compared to the conventional analysis method.

In modern society, the delivery service market has grown explosively due to rapid changes in social structure and the recent COVID-19 pandemic. Therefore, various problems such as injury to workers and an increase in human accidents are occurring due to the loading and unloading of parcels. In order to solve this problem, domestic company n is developing a “robot-based cargo loading and unloading system”. In developing a new technology system, quantitative reliability targets should be set for efficient operation and development. In this paper, reliability analysis was conducted through field data for the pneumatic gripper of the “robot-based cargo loading system”. The reliability of the failure data was analyzed to estimate the distribution parameters and MTTF. Random data was derived for the probability of occurrence of a failure with the estimated value. By repeating the simulation to predict the number and year of failures according to the estimated parameters of the probability distribution, it was proposed as a method that reflects realistic probabilities rather than calculating with simple arithmetic using the average MTTF previously used in the field.