The FOPLP method, which uses a square metal carrier to arrange semiconductor chips, offers significantly superior productivity and efficiency compared to conventional processes. However, metal carriers are prone to warping, dents and scratches due to thermal deformation, making surface inspection and correction work essential. Therefore, this study designed and fabricated a gantry guide capable of mounting an indicator and a vision module to effectively inspect the metal carrier surface and improve quality, then evaluated its performance. In the experiment, the gantry system’s performance was verified by evaluating its repeatability precision, and the vision module ensured data reliability through precision at four different magnifications.

This study was conducted to explore ways to improve the operation of NCS-based curricula, focusing on vocational education programs. The purpose is to design and establish an effective NCS-based vocational education curriculum and to apply it in educational settings. For the purpose of improving the operation of NCS-based curricula, this study first reviewed the relevant information and system operations presented on the National Competency Standards (NCS) website (http://www.ncs.go.kr/). This is because effective vocational education can only be achieved when accurate information regarding the curriculum and educational content is properly delivered. In conclusion, it is required to identify the problems in the design, composition, and operation of vocational education curricula, and to establish curriculum design strategies that can enhance the overall efficiency of vocational education programs. Based on the results of this study, the research examined the competency unit “Inspection of Aircraft Reciprocating Engine Fuel Systems” under the NCS categories — Major Category [15. Machinery], Subcategory [09. Aircraft Manufacturing], Minor Category [03. Aircraft Maintenance], and Detailed Category [03. Aircraft Reciprocating Engine Maintenance] — and designed the learning content for the NCS learning module “Inspecting the Boost Pump.” The designed practical training plan has the following characteristics: First, by incorporating additional content from the NCS competency unit, it was structured to be applicable in actual workplaces that operate aircraft equipped with reciprocating engines. Second, it aims to enhance troubleshooting skills required for the maintenance of aircraft reciprocating engines. Third, the contents were designed to align with the practical training environments of educational institutions while ensuring applicability to real-world work settings. Fourth, it was designed to allow learners to simultaneously practice content related to obtaining aviation-related certifications. This study is significant in that it designed a vocational education curriculum and proposed effective strategies for improving vocational education. However, there are certain limitations. The study did not include an empirical implementation or analysis of the results based on the designed instructional program. In addition, it did not develop curricula that reflect the specific characteristics of individual subjects within the field of NCS-based aircraft maintenance through the design of diverse course modules.

상수관로의 노후화는 수질 안전성 저하와 수자원 손실, 유지보수 비용 증가 등의 문제를 야기하며, 이에 따라 지중 매설관의 상태를 신속하고 정확하게 진단할 수 있는 기술의 중요성이 커지고 있다. 특히 내시경 영상을 활용한 관로 점검은 가장 보편적인 방식으로 자리 잡았으나, 판독자의 숙련도에 따라 해석 편차가 발생하고, 대량 데이터의 신속한 처리에는 한계가 있다. 이러한 배경에서 본 연구는 관종⋅관경⋅용도 등 상수관 메타데이터를 모델에 통합하고, 관로 내 결함의 존재 여부와 유형, 크기를 동시에 예측할 수 있는 다중과제 학습(Multi-task Learning) 기반 인공지능 모델을 제안한다. 제안한 모델은 두 개의 예측 헤드를 통해 결함 판별과 정량적 분류를 병행하도록 설계되었으며, SHAP 기반 분석을 통해 모델의 판단 근거가 상수관로의 실제 결함 특성과 일치함을 확인하였다. 이러한 접근은 수작업 판독의 부담을 경감하고, 관로 상태 기록의 표준화 및 정량화를 통해 예방 중심의 유지관리 전략 수립을 효과적으로 지원할 수 있다.

This study analyzed quarantine inspection records for imported ginger seed rhizomes (Zingiber officinale) in Korea from 2015 to 2024, utilizing the Pest Information System (PIS) of the Animal and Plant Quarantine Agency. The aim was to characterize the occurrence and trends of plant-parasitic nematodes associated with ginger planting and to provide management implications for risk-based phytosanitary measures. A total of 1,327 cargo consignments were analyzed. Plant-parasitic nematodes were found in 496 consignments (37%), comprising 13 different nematode taxa. Two regulated taxa, Meloidogyne spp. and Pratylenchus spp., were identified in 149 cases, leading to the destruction of the corresponding consignments in accordance with quarantine regulations. The remaining 11 taxa (347 cases) were classified as non-quarantine pests and were released. Root-knot nematodes (Meloidogyne spp.) were the most prevalent, accounting for over 80% of all detections, with M. incognita (191 cases), Meloidogyne spp. (128 cases), and M. javanica (68 cases) being the most common. Although Gunsan Port had the highest number of consignments, the detection rate was relatively higher at Incheon Port, suggesting possible differences among production areas or exporters. Overall, the findings indicate that imported ginger seed rhizomes can serve as a pathway for nematode introduction. To reduce the risk of field-level transmission, it is recommended to enhance pre-export lot management and implement nematode suppression measures, along with providing post-import guidance for growers, such as hot-water treatment or approved dips before planting.

With the increasing number of aging buildings, the importance of structural safety inspections has grown significantly. Traditional methods for inspecting welding defects, such as visual inspection and magnetic testing, rely heavily on human expertise, making them time-consuming, costly, and subjective. To address these limitations, thermographic technology has been introduced as a non-contact alternative, significantly reducing both time and cost. Furthermore, by incorporating AI, an objective and automated evaluation of welding defects can be achieved. In this study, we propose an AI-based thermographic approach for detecting welding defects. To validate the applicability of this method, a Mock-up Test was conducted. Specifically, 12 types of welding specimens with 4 welding part were prepared, generating a dataset of 6,500 thermographic images. Among 7 regression algorithms tested, RF and EXT were selected due to their superior performance. By ensemble learning these two models, we developed a robust welding defect measurement algorithm. To further verify its effectiveness, we applied the developed algorithm to 2 real projects, evaluating its applicability using 450 thermographic images. The results of this study demonstrate the feasibility of AI and thermographic technology in welding defect detection, highlighting its potential to enhance the efficiency and reliability of structural safety inspections in aging infrastructures.

연구는 내항여객선의 상가 주기를 조정함으로써 연료비를 포함한 운항비용의 절감 가능성을 분석하는 데 목적이 있다. 선박의 연료 효율에 영향을 미치는 요소 중 하나인 선체부착생물은 선박과 해수 간 마찰을 증가시켜 더 많은 연료 소모를 유발하는 것으로 알려 져있다. 선박안전법에 따라 감항성 유지 및 운항 안전을 위해 매년 정기검사 또는 제1종 중간검사를 받아야 하는 내항여객선은 일반적으 로 이러한 검사에 대비하여 연 1회의 상가 수리를 통해 선체부착생물을 제거한다. 이에 본 연구에서는 실제 운항 중인 3척의 내항여객선 을 대상으로 AIS 데이터, 항해일지 및 상가 수리 비용 등의 자료를 수집하고, 연간 표준운항비용을 산정하였다. 이를 바탕으로 MATLAB 기반의 시뮬레이션을 통해 각 선박의 상가 주기별 운항 비용 산출하여 비용 절감 효과를 분석하였다. 분석 결과, 선박별로 최대 비용 절 감 효과가 나타나는 시점이 상이하였으나, 연료비 절감과 추가 상가 비용 간의 균형점이 존재함을 확인하였다. 본 연구는 내항여객선의 선체 유지관리에 있어 상가 주기 조정이라는 운영 전략을 정량적으로 분석하였으며, 선사의 유지보수 계획 수립에 실질적인 근거를 제공 한다는 점에서 실무적 의의가 있다.

This paper addresses a scheduling problem aimed at minimizing makespan in a permutation flow shop with two machines and an inspection process that must be conducted at least once every certain number of outcomes from the first machine. A mathematical programming approach and a genetic algorithm, incorporating Johnson's rule and a specific mutation process, were developed to solve this problem. Johnson's rule was used to generate an initial population, while the mutation process ensured compliance with the inspection constraints. The results showed that within a computation time limit of 300 seconds, the mathematical programming approach often failed to provide optimal or feasible solutions, especially for larger job sets. For instance, when the process times of both machines were similar and the inspection time was longer, the mathematical programming approach failed to solve all 10 experiments with just 15 jobs and only had a 50% success rate for 100 jobs. In contrast, the proposed genetic algorithm solved all instances and delivered equal or superior results compared to the mathematical programming approach.

Detection and sizing of defects are very important for structure life management base on fracture mechanics. The non-destructive inspection techniques based on the induced current field measurement are newly developed. This paper describes the results obtained by these techniques for artificial surface defects. In the case of the RICFM technique, the potential drop distribution around a surface defects was measured as a smaller potential drop than that in a place without a defect. This potential drop showed a minimum value at the defect location, and the absolute value of this minimum value increases depending on the depth of the defect. In the case of the FEF technique, the potential difference distribution for surface defects was measured as a maximum at the location of the defect. This maximum value showed a difference depending on the depth of the defect.

지난 10년간 국내 고속도로의 관리 대상 구조물 수는 2013년 8,302개소에서 2023년 11,054개소로 약 25% 증가했다. 특히, 공용 20~30년 미만의 교량이 전체 교량의 약 40%를 차지하고 있으며, 이들 교량의 노후화가 향후 10년 내 집중적으로 발생할 것으로 예상 된다. 이에 따라 유지관리 비용이 급격히 증가할 것으로 전망된다. 효율적인 자산관리를 위해서는 상태평가 결과를 바탕으로 예측모델 을 적용하여 구조물의 성능과 생애주기 비용을 예측하는 것이 중요하다. 그러나, 유지관리에 따른 구조물 성능향상과 열화모델 적용 등 다양한 변수를 고려한 예측모델 적용할 때, 인력점검의 한계와 점검자의 주관적 판단에 따른 점검오차를 최소해야만 개별 구조물 의 현재 상태에 대한 정확한 평가가 가능할 것이다. 이와 관련하여 본 연구에서는 자산관리 개선을 위한 추진전략과 상태평가 신뢰성 확보를 위한 신기술 적용방안을 제시하고자 한다. 따라서, 교량 자산가치평가 정확도 향상을 위해 BIM(Building Information Modeling) 모델 제작 및 손상평가 AI(Artificial Intelligence) 기술을 적용한 ‘BIM 기반 외관조사망도 자동생성 시스템’을 통해 인력점검의 한계와 점검오차로 인한 문제를 개선하고자 하며, 점검/진단 자동화 기술을 구조물 유지관리 업무 시스템에 연계하여 손상정도를 시계열로 모 니터링하고, 최적 보수시기 및 공법 선정 의사결정에 활용할 수 있으며, 보수·보강 비용 및 조치편익을 분석하여 유지관리 사업계획 수립 시 활용할 수 있을 것으로 판단된다. 향후 ‘점검/진단 자동화 시스템‘을 고속도로 자산관리에 시범적으로 적용하여 실제 현장 점 검자의 사용성 검증과 시스템 운영방안 수립을 통해 효율적 자산관리를 위한 도로관리자의 의사결정을 지원할 수 있을 것으로 기대한다.

In South Korea, over 400,000 Non-building Structures are inadequately managed and exposed to potential risks due to insufficient inspection systems, leading to an increase in accidents and significant losses of life and property. Therefore, it is crucial for users to conduct proactive self-inspections to identify and mitigate potential hazards. This study reclassified Non-building Structures into four main categories by analyzing their structural characteristics and associated risks through statistical analysis. Among these, retaining walls, which account for the largest proportion, were systematically analyzed to identify common damage patterns. Based on this analysis, self-inspection checklists were developed for both non-experts and experts. The proposed process involves an initial visual inspection using a simple non-expert checklist, followed by a more detailed expert-level inspection if any anomalies are detected. The reliability of this process was validated through approximately 120 validation processes.

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.

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.

In this study, we present a sewer pipe inspection technique through a combination of active sonar technology and deep learning algorithms. It is difficult to inspect pipes containing water using conventional CCTV inspection methods, and there are various limitations, so a new approach is needed. In this paper, we introduce a inspection method using active sonar, and apply an auto encoder deep learning model to process sonar data to distinguish between normal and abnormal pipelines. This model underwent training on sonar data from a controlled environment under the assumption of normal pipeline conditions and utilized anomaly detection techniques to identify deviations from established standards. This approach presents a new perspective in pipeline inspection, promising to reduce the time and resources required for sewer system management and to enhance the reliability of pipeline inspections.

해상특수교량은 특수한 환경적 조건뿐 아니라 고주탑의 구조형식, 보호재로 쌓여있는 케이블 등 특 수한 형식을 가지고 있어 일반적인 육안전검으로 안전점검을 할 수 없는 사각지대가 존재한다. 주탑의 외부 손상상태 및 케이블의 손상에 대해서는 정밀안전점검에서도 점검이 되지 않는 경우가 대부분이 므로 이에 대한 대책 마련이 시급하다. 또한 해상특수교량에 대한 전문적인 경험과 기술이 부족한 관 리자도 대상교량의 손상과 이상거동을 직관적으로 확인하고 판단할 수 있는 지원체계가 필요하다. 이 에 본 연구에서는 해상특수교량 고주탑에 대한 손상정보를 파악하기 위하여 드론의 자동비행 기술을 개발하고 이를 이용하여 주탑 외부 균열 손상에 대한 안전점검을 실시하고 이를 분석하였다.

PURPOSES : This study aimed to analyze the primary maintenance procedures and safety inspection checklist characteristics for suspension bridges. The study referred to the current suspension bridge safety management manual and conducted an on-site safety inspection. By comparing and analyzing any missing or inadequate inspection and management procedures, the study identified major inspection and management areas requiring improvement, and proposed potential solutions. METHODS : The study referred to the current suspension bridge safety management manual and conducted on-site safety inspections. By comparing and analyzing any missing or inadequate inspection and management procedures, the study identified major inspection and management areas requiring improvement, and proposed potential solutions. RESULTS : The study found that suspension bridges are currently inefficiently managed compared to other facilities subject to more rigorous maintenance and safety inspection. Therefore, maintenance and safety inspection procedures require improvement. CONCLUSIONS : For effective safety management and to reduce potential accident risk factors arising from negligent management, major improvements were suggested. Scientific maintenance and safety management could be achieved by incorporating enhancements into statutory requirements and improving management and inspection procedures. This long-term approach is likely to be more economical than the current methods, which lead to higher maintenance and repair costs and increased social costs from traffic accidents.