In order to prevent accidents via defective ammunition, this paper analyzes recent research on ammunition life prediction methodology. This workanalyzes current shelf-life prediction approaches by comparing the pros and cons of physical modeling, accelerated testing, and statistical analysis-based prediction techniques. Physical modeling-based prediction demonstrates its usefulness in understanding the physical properties and interactions of ammunition. Accelerated testing-based prediction is useful in quickly verifying the reliability and safety of ammunition. Additionally, statistical analysis-based prediction is emphasized for its ability to make decisions based on data. This paper aims to contribute to the early detection of defective ammunition by analyzing ammunition life prediction methodology hereby reducing defective ammunition accidents. In order to prepare not only Korean domestic war situation but also the international affairs from Eastern Europe and Mid East countries, it is very important to enhance the stability of organizations using ammunition and reduce costs of potential accidents.

This study was conducted to analyze relationships between depression indices, mini nutritional assessment scores, and nutritional quotients among 80 elderly in Yangpyeong-gun and to identify factors that help prevent depression and malnutrition. Nutrition assessment scores were low in the high-risk group (PHQ-9 score 10), and nutritional quotient scores were lower in the high-risk group than in the normal group (PHQ-9 score 4). Interestingly, the consumption frequencies of fruits, eggs, and nuts were low in the high-risk group, and subjective health awareness, dental condition, and sleep were poorer. The total PHQ-9 score was correlated with malnutrition, body mass index, calf circumference, weight change, independent daily living, reduced meal amount, water intake, and the need for help when eating. Analysis of correlations between items of the PHQ-9 and nutritional status evaluation indices showed that a self-perceived feeling of depression, low energy, difficulty controlling sleep or appetite, negative thoughts (e.g., failure, disappointment), and difficulty concentrating were negatively correlated with total nutritional status scores. These results show that attention is required when food or water intake decreases and that deviation from normal sleep and appetite cycles flags the need to prepare guidelines to prevent depression.

In factory automation, efforts are being made to increase productivity while maintaining high-quality products. In this study, a CNN network structure was designed to quickly and accurately recognize a cigarette located in the opposite direction or a cigarette with a loose end in an automated facility rotating at high speed for cigarette production. Tobacco inspection requires a simple network structure and fast processing time and performance. The proposed network has an excellent accuracy of 96.33% and a short processing time of 0.527 msec, showing excellent performance in learning time and performance compared to other CNN networks, confirming its practicality. In addition, it was confirmed that efficient learning is possible by increasing a small number of image data through a rotation conversion method.

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.

The use of heat exchangers in various applications such as chemical, air conditioning systems, fuel processing, and power industries is increasing. In order to improve the performance of the heat exchanger, the problem of bonding quality of the copper tube, which is a major member, is emerging. However, since the copper tube is in the form of a pipe, it is difficult to identify internal defects with external factors. In this study, a thermal imaging camera was used to develop and verify an algorithm for detecting defects in the brazing part, and in the process, the brazing performance characteristics were analyzed according to the electrode position, and finally, a learning model was developed and performance evaluation was performed. It was confirmed that the method of supplying heat to the base material and melting the filler metal through the heat transfer effect is more effective than supplying heat input to the filler metal in the bonding process of copper tubes through high-frequency induction heating brazing. Thermal image data was used to develop a defect discrimination model, and 80% of training data and 20% of test data were selected, and a neural network-based single-layer copper tube brazing defect discrimination model was developed through k-Flod cross-validation., the prediction accuracy of 95.2% was confirmed as a result of the error matrix analysis.

본 연구의 목적은 피부전문가들이 경험한 고객 불량행동이 감정부조화 및 서비스 사보타주에 미치는 영향에 관한 연구이다. 연구의 목적달성을 위해 서울. 경기지역 피부전문가 383명을 연구대상으로 선정하였다. 분석방법은 빈도분석, 요인분석, 신뢰도분석, 상관관계분석, 다중회귀분석을 실시하였으며, 이 와 같은 연구절차를 거쳐 다음과 같은 연구결과를 도출하였다. 고객 불량행동이 감정부조화에 정적(+)상관 관계가 나타났고, 감정부조화의 감정억제가 서비스사보타주에 영향을 미치는 것으로 나타났다.

WDM(Wavelength Division Multiplexing) is called a wavelength division multiplexing optical transmission method and is a next-generation optical transmission technology. Case company F has recently developed and sold PLC(Planar Lightwave Circuit), a key element necessary for WDM system production. Although Chinese processing companies are being used as a global outsourcing strategy to increase price competitiveness by lowering manufacturing unit prices, the average defect rate of products manufactured by Chinese processing companies is more than 50%, causing many problems. However, Chinese processing companies are trying to avoid responsibility, saying that the cause of the defect is the defective PLC Wafer provided by Company F. Therefore, in this study, the responsibility of the PLC defect is clearly identified through estimating the defect rate of PLC using the sampling inspection method, and the improvement plan for each cause of the PLC defect for PLC yeild improvement is proposed. The result of this research will greatly contribute to eliminating the controversy over providing the cause of defects between global outsourcing companies and the head office. In addition, it is expected to form a partnership with Company F and a Chinese processing company, which will serve as a cornerstone for successful global outsourcing. In the future, it is necessary to increase the reliability of the PLC yield calculation by extracting more precisely the number of defects.

In this paper, we propose dead pixel detection and compensation method using nonlinear estimator for infrared camera. Infrared camera has dead pixel that is abnormal output values due to complex factors such as manufacturing process, electronic parts and so on. Dead pixels are able to affect detecting a small target. So, It needs detection and Compensation process. However, after Compensation, some dead pixels are remained and detected by the human. They are soft dead pixel. The key idea of this proposed method, detecting soft dead pixels, is that design a nonlinear estimator using image data characteristics. This propose is able to not only detect soft dead pixels but also pixel Compensation that reflects infrared camera output characteristics well.

I propose an algorithm to detect defects in the production of wire mesh using computer image processing. The process is explained as follows, First reading consecutive frames coming through the camera, then the preprocessing process is performed. Second calculate the absolute difference between the two images to distinguish the moving wire mesh from the unnecessary background image. Third based on the past moving data of the welded wire mesh, predict and track future movement. As a result of observing the samples of some defective welded wire mesh products, it was confirmed that the horizontal line of the defective wire mesh had a higher height value of the tracked wire netting. Therefore it is possible to judge whether there is a defect or not at the same time without any additional process to judge. Finally, shear strength test were performed on the welds determined to be normal products by the algorithm proposed in this paper, so that I could verify the reliability and validity of the proposed algorithm.

본 연구에서는 아파트단지 내 불량한 식생공간을 보수하거나 다양한 식생으로의 재조성하여 녹색의 질을 높이기 위한 방안으로 아파트 단지 내 조경관리 현황을 조사하고 식재지반인 토양상태를 파악하여 합리적인 계획 수립을 위한 기초 자료화 하는 것을 목적으로 하였다. 아파트 준공시점을 기준으로 동일한 규격의 수목이 동시에 식재되는 것을 감안해 준공연도를 5년 단위로 구분하고 식재기간 경과에 따른 수목생장과 토양성분의 차이를 분석하였다. 전체 조사대상지 9개소에 모두 식재된 수종 중 침엽수 4종과 활엽수 4종의 생육상태를 조사하여 한 결과 침엽수 중 가이즈까향나무 소나무, 활엽수 중에서는 꽃사과나무가 5년 이상의 식재기간이 경과되었을 때 가장 생육이 좋았다가 10년 이상 되었을 때는 생장이 감소하였다. 측백나무와 단풍나무는 10년 이상 된 지역의 수목이 생육이 가장 좋아 비교적 생장을 위한 기간이 많이 요구되는 수종이었다. 수목은 식재되어 있으나 지표면이 나지가 되어있는 지점의 토양경도를 분석한 결과 준공 후 5년 이상이 되면 20㎜ 이상의 답압 된 상태가 되는 것으로 조사되었고 토양이화학성은 준공 초기부터 지속적으로 pH는 높고 유기물함량은 비옥한 토양의 1/3 정도로 추가 시비가 필요한 상태였다. 본 결과를 토대로 나지부분의 재식재시에는 식재지 주변 배수로 설계와 토심 50㎝ 정도를 객토하고 왕겨 등의 유기질재료를 혼합하는 정도의 토양 개량 계획을 추가하여 수목 하부에 지피식물을 식재하는 것만으로 토양의 답압과 토양건강성을 회복하는 방안이 될 것이라고 사료된다.