가로수의 수종에 따른 결함 요인을 분석하고자 인천광역시의 가로수 느티나무, 백합나무, 양버즘나무 와 왕벚나무 4수종을 대상으로 시각적 수목평가를 실시하고 위해 정도를 파악하였다. 느티나무는 변재 부의 부후 상처가 다른 수종에 비해 많고 상처면적이 큰 개체도 다수 발견되나 줄기에서 관찰되는 공동 의 수는 적고 균류의 자실체 발생률 또한 낮았다. 반면 타진음 검사에서 이상 소견이 가장 많이 관찰되었 고 동일세력 줄기의 발생률과 분기지점의 결함 수관에서 차지하는 고사지의 비율 등은 다른 수종에 비 해 높다. 또한 줄기를 옥죄는 뿌리의 발생률은 비교적 낮고 수관이 차도 또는 보행로 방향으로 편중된 경향을 보였다. 백합나무는 구조적으로 안정된 수형을 이루고 있으며, 변재부의 부후상처 발생률, 타진음 검사결과 동일세력 줄기의 결함 등은 다른 수종에 비해 건전한 것으로 분석되었다. 그러나 백합나무 는 줄기가 직립하는 성질이 강하여 수관이 편향된 개체의 비율은 비교적 낮은 편이다. 양버즘나무는 세장비가 비교적 높고 줄기가 약하게 기울어 자라는 특성이 관찰되나 활관비가 높아 활력을 유지하는 것으로 판단된다. 줄기에 발생하는 결함 요인들도 비교적 낮은 수준이어서 건강성이 높으나 위험하지 않을 정도로 줄기가 기울어 수관이 차도 방향으로 편중된 개체의 비율은 높다. 왕벚나무는 세장비가 4수종 중 가장 낮아 초살이 잘 발달하는 수종이며, 활관비와 줄기의 기울기 각도는 비교적 안정한 편이 다. 그러나 부후와 공동의 발생률은 다른 수종에 비해 유의적으로 높아 상처에 취약한 수종임을 확인할 수 있다. 수종별 위험도 등급을 구분한 결과 느티나무와 왕벚나무의 결함 발생률과 위험도가 비교적 높게 나타났으며, 백합나무와 양버즘나무는 상대적으로 도로환경의 적응도가 높은 것으로 판단된다. 시각적 수목평가는 조사할 나무의 수량이 많고 선형으로 식재된 가로수의 위험성 평가에 흔히 통용되 는 방법인데, 향후 가로수의 건강한 생육과 안전한 관리를 위해 평가 항목을 개발하고 평가 기준을 표준 화하는 등의 추가적인 연구가 필요하다.

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.

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.

This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

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.