This study introduces a novel approach for identifying potential failure risks in missile manufacturing by leveraging Quality Inspection Management (QIM) data to address the challenges presented by a dataset comprising 666 variables and data imbalances. The utilization of the SMOTE for data augmentation and Lasso Regression for dimensionality reduction, followed by the application of a Random Forest model, results in a 99.40% accuracy rate in classifying missiles with a high likelihood of failure. Such measures enable the preemptive identification of missiles at a heightened risk of failure, thereby mitigating the risk of field failures and enhancing missile life. The integration of Lasso Regression and Random Forest is employed to pinpoint critical variables and test items that significantly impact failure, with a particular emphasis on variables related to performance and connection resistance. Moreover, the research highlights the potential for broadening the scope of data-driven decision-making within quality control systems, including the refinement of maintenance strategies and the adjustment of control limits for essential test items.

Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.

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.

Recently, the development of computer vision with deep learning has made object detection using images applicable to diverse fields, such as medical care, manufacturing, and transportation. The manufacturing industry is saving time and money by applying computer vision technology to detect defects or issues that may occur during the manufacturing and inspection process. Annotations of collected images and their location information are required for computer vision technology. However, manually labeling large amounts of images is time-consuming, expensive, and can vary among workers, which may affect annotation quality and cause inaccurate performance. This paper proposes a process that can automatically collect annotations and location information for images using eXplainable AI, without manual annotation. If applied to the manufacturing industry, this process is thought to save the time and cost required for image annotation collection and collect relatively high-quality annotation information.

The purpose of this study is to analyze the effect of temperature and humidity on the measured Particulate Matter (PM) concentrations recorded by PMS5003T, a low-cost light scattering type measuring tool. A regression analysis was performed on the ratio of PM concentrations measured by the light scattering method and the beta-ray absorption method according to temperature and humidity in an outdoor environment. As the temperature decreased, the PM concentration ratio increased, and this tendency intensified below 0oC. As the humidity increased, the PM concentration ratio increased, but the effect was less than the temperature effect. The coefficients of determination for temperature and humidity were R2 = 0.325 and 0.003, respectively, and the effects of temperature and humidity on the measured values w ere formulated and compensated for. As a result of the compensation, R2, relative precision, accuracy and RMSE improved from 0.927 to 0.958, from 91.183% to 96.651%, from 31.383% to 74.058%, and from 13.517 μg/m³ to 6.690 μg/m³, respectively. Finally, results from this study indicate that the reliability of the low-cost light scattering type PM sensor can be improved by applying the temperature and humidity compensation method.

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.

The premature failure of the universal joint connecting the drive gear box and the cooling fan caused a deterioration in serviceability and operability. Universal joint is a device that transmits engine power to a cooling fan. Internal pin breakage and shaft separation can cause secondary damage such as cooling fan malfunction and radiator damage caused by component failure. The purpose of this paper is to analyze the damage phenomenon of universal joints caused by bundles in SPVs and to improve them. In order to verify the improvement, a single part test and a system conformance test were conducted, and durability test was conducted to confirm the improvement effect on the improved prototype. Through these, the effects of increasing the durability of the improved product were estimated.

Recently, a study of prognosis and health management (PHM) was conducted to diagnose failure and predict the life of air craft engine parts using sensor data. PHM is a framework that provides individualized solutions for managing system health. This study predicted the remaining useful life (RUL) of aeroengine using degradation data collected by sensors provided by the IEEE 2008 PHM Conference Challenge. There are 218 engine sensor data that has initial wear and production deviations. It was difficult to determine the characteristics of the engine parts since the system and domain-specific information was not provided. Each engine has a different cycle, making it difficult to use time series models. Therefore, this analysis was performed using machine learning algorithms rather than statistical time series models. The machine learning algorithms used were a random forest, gradient boost tree analysis and XG boost. A sliding window was applied to develop RUL predictions. We compared model performance before and after applying the sliding window, and proposed a data preprocessing method to develop RUL predictions. The model was evaluated by R-square scores and root mean squares error (RMSE). It was shown that the XG boost model of the random split method using the sliding window preprocessing approach has the best predictive performance.

These studies were conducted to evaluate developmental competence of follicular oocyte collected from the ovaries of Hanwoo cows with the high offspring meat quality (1++ and 1+ grade). Cumulus oocyte complexes from individual cows were matured, fertilized and cultured using protocols of in-vitro maturation (IVM), in-vitro fertilization (IVF) and in-vitro culture (IVC). The rates of blastocyst development from Hanwoo cows with the offspring meat quality grades of 1++ and 1+ were 18.6 and 21.2%, respectively. The rates of blastocyst development were 26.3, 20.7, 20.7, 17.2 and 31.2% from Hanwoo cows with the meat quality grades of 1++, 1+, 1, 2 and 3, respectively. Fiftyseven transferable embryos were recovered from 11 Hanwoo donor cows (5.2/head) with the high offspring meat quality grades of 1++ and 1+ in vivo, and the pregnancy rate after embryo transfer was 61.1%. In conclusion, these results suggest that in vitro embryo production from the ovaries of cows with the high meat quality grades using individual culture system can be used an efficient method for livestock improvement. In addition, for the successful industrialization of embryo transfer, conception rate should be improved.

본 연구는 기구등에 오염된 E. coli와 S. aureus를 제어 하기 위해 이산화염소의 농도별 접촉시간에 따른 살균소 독력을 평가하여 살균예측모델을 개발하였다. E. coli의 경우 초기균수가 9.13 log CFU/mL이었고, 청정조건에서 5 ppm으로 1분, 3분, 5분 처리한 결과 각각 0.04, 0.07, 0.10 log CFU/mL의 감소값을 나타내었다. 20 ppm을 처리한 결 과 각각 0.74, 0.79, 0.84 log CFU/mL의 감소값을 나타내었다. 또한 CCD에 의한 최대농도 35 ppm으로 처리한 결과 각각 2.49, 2.70, 3.65 log CFU/mL의 감소값을 나타내었다. S. aureus의 경우 초기균수가 8.70 log CFU/mL이었고, 청정조건에서 5 ppm으로 1분, 3분, 5분 처리한 결과 각각 0.14, 0.28, 0.36 log CFU/mL의 감소값을 나타내었다. 20 ppm을 처리한 결과 각각 0.66, 0.79, 0.90 log CFU/mL의 감소값을 나타내었다. 또한 CCD에 의한 최대농도 35 ppm 으로 처리한 결과 각각 4.59, 5.25, 5.81 log CFU/mL의 감소값을 나타내었다. 따라서 이산화염소의 살균소독력 평 가결과는 E. coli와 S. aureus에 대하여 식품의약품안전처 살균소독력 기준에 모두 만족하는 것으로 나타났다. 살균 예측모델의 경우, R2값이 모두 0.98 이상으로 두 균주에 대해 모두 높은 적합성을 보였다. 본 연구에서 개발된 이산화염소의 살균예측모델을 식품산업 적용을 위한 기초자 료로 활용함으로써 E. coli와 S. aureus를 적절한 농도와 접촉시간으로 제어할 수 있을 것으로 사료된다.

Shelter that communication equipment and on-equipment material are mounted on is transported by airplane or vehicle, it has a function such as waterproof and shielding EMI. When the shelter is transported by car or helicopter, it is shocked by vibration and crash with the ground. So, three skids are attached to bottom of the shelter to reduce the shock. To confirm the durability of the structure of shelter, parallel drop and rotating drop test were done in accordance with KDS 0000-0000. However, the damage was discovered in the shelter. So, stiffening plate was added to skid and panel of partition wall to obtain durability and changed the shape of rubber buffer. In this paper, the cause of deformation and damage in the shelter was analyzed and improved shape through the structural analysis was verified.

경수로 원전을 대상으로 원전 내 방사화 대상 물질인 스테인리스강, 탄소강 및 콘크리트의 불순물 정보 적용여부에 따른 방 사화 핵종 재고량을 계산하였다. 본 연구에서 탄소강은 압력용기 물질에 사용되었고, 스테인리스강은 압력용기 내부 물질에 사용되었으며, 일반 콘크리트가 생체 차폐체에 사용되었다. 금속 물질에 대해서는 참고자료 1개의 불순물 함량 정보를 적용 하였고, 콘크리트 물질에서는 참고자료 5개의 불순물 함량 정보를 적용하여 평가를 수행하였다. 방사화 핵종 재고량 전산해 석 시 중성자속 계산에는 MCNP 전산코드를, 방사화 계산에는 FISPACT 전산코드를 각각 사용하였다. 계산 결과, 금속 물질 에서 불순물을 포함한 경우가 그렇지 않은 경우보다 비방사능이 2배 이상 높았으며, 특히 콘크리트에서는 불순물을 포함한 경우가 그렇지 않은 경우보다 최대 30배 이상 비방사능이 높게 계산되었다. 방사화 핵종의 생성반응과 재고량을 분석한 결 과, 금속 구조물에서는 불순물 중 Co원소와 중성자에 의해 생성되는 방사화 핵종인 Co-60이, 콘크리트에서는 불순물 중 Co, Eu 원소와 중성자에 의해 생성되는 방사화 핵종인Co-60, Eu-152, Eu-154 이 방사성폐기물 준위 결정에 큰 영향을 미치고 있 음을 확인하였다. 본 연구의 결과는 원전 해체 계획 수립 시 방사화 핵종 재고량 평가 및 규제에 활용될 수 있을 뿐 아니라, 해체를 고려한 원전 또는 원자력시설의 설계 단계에서도 참고자료로 활용 될 것으로 판단된다.

Safety accident rate of small-medium construction site is high. because of lack of safety management system, lack of safety management capacity, lack of investment for safety, Owner's insufficient awareness about safety. In order to improve this, Currently in Occupational Safety and Health Act, Construction site of amounts more than 300 million won less than 120 billion (architectural),150 billion won(civil) mandatory subject to the technical guidance on construction accident prevention. Context of construction accident causes with construction accident rate relationship analysis and case analysis of technical guidance, through a survey of stakeholders in the technical guidance drawn the problems of the construction accident prevention technical guidance system and ways to improve on this.

The aim of this study was to determine the relationship between the coat color appearance of Korean brindle cattle and the changes of relevant hormone levels that may affect the hair pigmentation during different stages of growth and maturation. In mature cattle, levels of both ACTH and DHEA in Korean brindle cattle with brown color were significantly higher than those with black color (p<0.05). Levels of α-MSH in Korean brindle cattle with whole brindle (≧50%) color were significantly higher than those with brown color (p<0.05). In calves of Korean brindle cattle at 2 to 6 months, the concentration of estradiol was significantly higher in calves with whole brindle color than those with part brindle color (p<0.05), when the coat color was confirmed. After 6 month of coat color confirmation, levels of testosterone and ACTH increased in calves with part brindle color and were significantly higher than those with whole brindle color (p<0.05). In calves of Korean brindle cattle at 1 or 2 months, there were no significant differences in hormone levels of estradiol, ACTH, DHEA and α-MSH between the calves with brindle color and brown color, except estradiol before brindle color appearance. Changes of relevant hormone levels at different stage of growth and maturation may affect the pigmentation of coat during the development of cattle. In addition to the current study correlating the different coat colors with relevant hormone levels, investigation of the coat color associated genes expressed in Korean brindle cattle may further clarify the mechanisms of coat color changes during their development.

This research was designed to find out how imagery training program influence students' motor skills in high jump and how various teaching techniques should be applied to improve students' motor learning. To achieve the purpose of the study, ninety studen