The purpose of this study is to produce virtual models of women aged in their 60s and to implement the virtual clothing with jackets. We referred to 3D images of standard and obese body types from the 8th Size Korea and attempted to create avatars based on their images through the various trials. Final virtual models were made to reflect the appearance of women in their 60s. For the standard body type, a 3D image with average body measurements was selected. Based on numerous trials aimed at turning her image into an avatar, the auto-converted avatar on CLO 3D was slimmer than the woman in the original image, and hence it was not suitable for the virtual model. After blending, we converted the image into an uneditable avatar for which only the joint points could be moved, thereby creating an avatar that was identical to the original image. We also selected an image of an obese woman with a “beer bottle” body shape from the 8th Size Korea. We created an avatar that resembled her shape by also converting it into an uneditable avatar for which only joint points could be moved. To use these avatars in virtual clothing, we removed masks of avatars and made faces, hair styles, and skin tones representing women in their 60s. The moderately-sized classic jackets were smooth on both virtual models and fitted satisfactorily. This study demonstrated the applicability of virtual model production of various body types or ages in special clothing studies.

In recent automated manufacturing systems, compressed air-based pneumatic cylinders have been widely used for basic perpetration including picking up and moving a target object. They are relatively categorized as small machines, but many linear or rotary cylinders play an important role in discrete manufacturing systems. Therefore, sudden operation stop or interruption due to a fault occurrence in pneumatic cylinders leads to a decrease in repair costs and production and even threatens the safety of workers. In this regard, this study proposed a fault detection technique by developing a time-variant deep learning model from multivariate sensor data analysis for estimating a current health state as four levels. In addition, it aims to establish a real-time fault detection system that allows workers to immediately identify and manage the cylinder’s status in either an actual shop floor or a remote management situation. To validate and verify the performance of the proposed system, we collected multivariate sensor signals from a rotary cylinder and it was successful in detecting the health state of the pneumatic cylinder with four severity levels. Furthermore, the optimal sensor location and signal type were analyzed through statistical inferences.

In the military, ammunition and explosives stored and managed can cause serious damage if mishandled, thus securing safety through the utilization of ammunition reliability data is necessary. In this study, exploratory data analysis of ammunition inspection records data is conducted to extract reliability information of stored ammunition and to predict the ammunition condition code, which represents the lifespan information of the ammunition. This study consists of three stages: ammunition inspection record data collection and preprocessing, exploratory data analysis, and classification of ammunition condition codes. For the classification of ammunition condition codes, five models based on boosting algorithms are employed (AdaBoost, GBM, XGBoost, LightGBM, CatBoost). The most superior model is selected based on the performance metrics of the model, including Accuracy, Precision, Recall, and F1-score. The ammunition in this study was primarily produced from the 1980s to the 1990s, with a trend of increased inspection volume in the early stages of production and around 30 years after production. Pre-issue inspections (PII) were predominantly conducted, and there was a tendency for the grade of ammunition condition codes to decrease as the storage period increased. The classification of ammunition condition codes showed that the CatBoost model exhibited the most superior performance, with an Accuracy of 93% and an F1-score of 93%. This study emphasizes the safety and reliability of ammunition and proposes a model for classifying ammunition condition codes by analyzing ammunition inspection record data. This model can serve as a tool to assist ammunition inspectors and is expected to enhance not only the safety of ammunition but also the efficiency of ammunition storage management.

빠르게 발전하는 이미지 인식 기술에도 불구하고 표 형식의 문서와 수기로 작성된 문서를 완벽하게 디지털화하기에는 아직 어려움이 따른다. 본 연구는 표 형식의 수기 문서인 선박 항해일지를 작성하는 데에 사용되는 규칙을 이용하여 보정 작업을 수행함으로 써 OCR 결과물의 정확도를 향상시키고자 한다. 이를 통해 OCR 프로그램을 통하여 추출된 항해일지 데이터의 정확성과 신뢰성을 높일 것 으로 기대된다. 본 연구는 목포해양대학교 실습선 새누리호의 2023년에 항해한 57일간의 항해일지 데이터를 대상으로 OCR 프로그램 인 식 후 발생한 오류를 보정하여 그 정확도를 개선하고자 하였다. 이 모델은 항해일지 기재 시 고려되는 몇 가지 규칙을 활용하여 오류를 식별한 후, 식별된 오류를 보정하는 방식으로 구성하였다. 모델을 활용하여 오류를 보정 후, 그 효과를 평가하고자 보정 전과 후의 데이터 를 항차별로 구분한 후, 같은 항차의 같은 변수끼리 비교하였다. 본 모델을 활용하여 실제 셀 오류율은 약 11.8% 중 약 10.6%의 오류를 식 별하였고, 123개의 오류 중 56개를 개선하였다. 본 연구는 항해일지 중 항해정보를 기입하는 Dist.Run부터 Stand Course까지의 정보만을 대 상으로 수행하였다는 한계점이 있으므로, 추후 항해정보 뿐만 아니라 기상정보 등 항해일지의 더 많은 정보를 보정하기 위한 연구를 진 행할 예정이다.

본 연구는 대도시에서 미세먼지 없는 학교 부지를 찾는 Model Eliciting Activity (이하 MEA) 활동을 통해 고 등학교 학생들의 문제 해결 특성을 조사하기 위한 것이다. 5차시로 개발된 MEA 활동에 79명의 고등학교 2학년 학생 들이 참여 하였으며, MEA 활동지를 주요 데이터로 수집하였다. 학생들이 작성한 활동지의 개방형 질문에 대한 답을 기반으로 학생들의 문제 해결 모델을 귀납적 및 질적 방법으로 분석하였다. 먼저 학생들이 다른 데이터보다 어떤 데이 터를 우선적으로 사용했는지 순서를 분석한 후 주어진 데이터 세트를 어떻게 상호 연결하여 순서를 결정하는지 분석하 였다. 분석결과 학생들은 미세먼지 배출량이 많은 곳을 기피하기 위해 미세먼지 배출농도, 산업단지 분포 등 미세먼지 와 직접적으로 관련된 데이터를 먼저 활용하는 경향이 있음을 알 수 있었다. 흥미롭게도 MEA 활동에서 고등학생의 문 제 해결 특성은 매우 다양하여 76명의 학생이 총 61가지 유형의 문제 해결 모델을 제작한 것으로 나타났다. 문제를 해 결하기 위해 동일한 순서의 데이터를 사용하는 학생의 최대 수는 6명으로 학생들의 문제 해결 방법은 매우 다양함을 보여준다. 그러나 공통적으로 미세먼지 농도가 높은 곳을 제외하는 방법으로 미세먼지 배출과 직접적으로 관련된 데이 터를 먼저 선택하는 특성을 보였다.

The Fourth Industrial Revolution and sensor technology have led to increased utilization of sensor data. In our modern society, data complexity is rising, and the extraction of valuable information has become crucial with the rapid changes in information technology (IT). Recurrent neural networks (RNN) and long short-term memory (LSTM) models have shown remarkable performance in natural language processing (NLP) and time series prediction. Consequently, there is a strong expectation that models excelling in NLP will also excel in time series prediction. However, current research on Transformer models for time series prediction remains limited. Traditional RNN and LSTM models have demonstrated superior performance compared to Transformers in big data analysis. Nevertheless, with continuous advancements in Transformer models, such as GPT-2 (Generative Pre-trained Transformer 2) and ProphetNet, they have gained attention in the field of time series prediction. This study aims to evaluate the classification performance and interval prediction of remaining useful life (RUL) using an advanced Transformer model. The performance of each model will be utilized to establish a health index (HI) for cutting blades, enabling real-time monitoring of machine health. The results are expected to provide valuable insights for machine monitoring, evaluation, and management, confirming the effectiveness of advanced Transformer models in time series analysis when applied in industrial settings.

Data commentary is an important text type in research articles; however, its discourse model is often challenging to access because it is embedded in the upper genres such as textbook, weather forecast, and journal article. This study aims to establish a discourse model of data commentary, with a focus on academic research papers in Economics and Business administration journals. To accomplish this, this study employs Move analysis and SF-MDA(Systemic Functional-Multimodal Discourse Analysis) to investigate the moves of data commentaries and the metafunctional meanings of each step. The results indicate that the data commentary discourse model consists of three moves: (1) summarizing the topic and methodology, (2) representing figure and numbers, and (3) analyzing and commenting on results. Additionally, 22 steps are identified for each move that creates metafunctional meaning: ideational, interpersonal, and textual.

Welding is one of representative manufacturing processes in the industrial field. Cryogenic storage containers are also manufactured through welding, and conversion to laser welding is issue in the field due to many advantages. Since welding causes thermal-elastic deformation, design considering distortion is required. Prediction of distortion through FEM is essential, but laser welding has difficulties in the field because there is no representative heat source model. The author presented the model that can cover various models using a multi-layer heat source model in previous studies. However the previous study has a limitation which is a welding heat source model must be derived after performing bead on plate welding. Thus this study was attempted to estimate the welding heat source parameters by comparing the shape of bead under various conditions. First, the difference between penetration shape and welding heat source parameters according to welding power was analyzed. The radius of the welding heat source increased according to the welding power, and the depth of the welding heat source also increased. The correlation between the penetration shape and the welding heat source parameter appears at a similar rate, however the follow-up research is necessary with more model data.

Ball stud parts are manufactured by a cold forging process, and fastening with other parts is secured through a head part cutting process. In order to improve process quality, stabilization of the forging quality of the head is given priority. To this end, in this study, a predictive model was developed for the purpose of improving forging quality. The prediction accuracy of the model based on 450 data sets acquired from the manufacturing site was low. As a result of gradually multiplying the data set based on FE simulation, it was expected that it would be possible to develop a predictive model with an accuracy of about 95%. It is essential to build automated labeling of forging load and dimensional data at manufacturing sites, and to apply a refinement algorithm for filtering data sets. Finally, in order to optimize the ball stud manufacturing process, it is necessary to develop a quality prediction model linked to the forging and cutting processes.