병충해의 조기 발견과 그에 따른 조치의 중요성은 농업 및 생태계 보전에 있어서 핵심적이다. 그러나 초기에는 일반적인 카메라나 센서로는 변화의 정도를 관측하기 어렵다. 이러한 한계를 극복하기 위해 초분광 모듈을 활용하여 파장대별 식 물 데이터를 관측함으로써, 딥러닝 모델을 통해 가로수 식생의 건강 상태를 판별, 병충해 여부를 초기에 확인 가능하다. 이를 통해 조기에 병충해에 대해 조치함으로써 더 큰 피해를 방지할 수 있다. 이러한 접근 방식은 농업 및 생태학 분야 에서 식물의 건강을 모니터링하고 보전하는 데 적극적으로 연구되고 있다.

This study analyzes the importance-performance analysis (IPA) of the 10 dimensions of the smart construction safety management system, and analyzes which dimensions are important and which dimensions are performing to determine key improvement tasks, incremental improvement tasks, Maintenance and reinforcement tasks and continuous maintenance tasks were derived. Among the 10 dimensions of the smart construction safety management system, the dimensions that are recognized as important by all field managers and field workers and have high performance are the automatic risk displacement measurement system, smart environmental sensor system, and heavy equipment seizure prevention system. However, areas that were perceived as having high importance but low performance were worker location tracking systems, smart safety helmet chin muscles, and smart safety ring fastening. Among the smart construction safety management systems perceived by field managers, areas for key improvement with high importance and low performance included worker location tracking system and smart safety ring fastening. Among the smart construction safety management systems perceived by field workers, the area for key improvement with high importance and low performance was the automatic risk displacement measurement system.

In response to the global transition towards carbon neutrality, there's an increasing emphasis on sustainable energy solutions, with offshore wind power playing a crucial role, especially in South Korea. This study presents an AI-based safety management system specifically designed for offshore wind operators. At the heart of this system is a machine learning algorithm that processes sensor data to automatically recognize human behavior and improve the accuracy of predicting worker actions and conditions. Such predictive analytics not only refines the analysis of behavioral patterns, but also increases the effectiveness of accident prevention. The results of this research are expected to significantly improve safety measures in offshore wind facilities and further sustainable energy initiatives.

비임상시험관리기준에서 독성시험 전 분석법 밸리데이션은 농도 설정 및 시료 제조 측면에서 중요하다. 시험기관에서는 의뢰받은 시험물질 2종에 대한 밸리데이션을 고성능액체크로마토그래피를 이용 하여 수행한 결과 특이성, 시스템 적합성, 직선성, 일내 재현성, 균질성, 안정성, 농도분석, 품질관리를 판정 기준에 만족하는 분석방법을 확립 및 검증하였다. 하지만 의뢰기관의 시험성적서상 표준물질 함량은 시험 기관의 결과보다 1.34배, 1.17배 높은 결과로 나타나 비임상시험관리기준에 적합한 분석법 밸리데이션을 통한 결과 도출이 신뢰성과 안정성 확보 측면에서 중요함을 확인하였다.

도로의 포장 상태의 노후화나 관리미흡으로 인하여 시민의 사유 재산 중 주요한 요소인 자동차 등의 손상이나 자동차 사고 로 이어질 수 있어 큰 사회적 비용이 발생할 뿐 아니라, 시민들의 불편과 불만을 초래할 수 있다. 최근 도로 포장의 경우 포트홀 발생 건수와 그에 따른 민원 및 소송 건수가 증가해 행정력 및 예산이 낭비되고 있으며, 서울시의 경우 포장도로 노후화 추이가 증가함에 따라 유 지 관리 비용 또한 증가하고 있다. SOC 시설물 안전성 강화에 대한 사회적 요구는 지속적으로 증가하고 있어 한정된 예산의 효율적 활용을 위한 첨단 유지관리기술 도입이 시급하다.

The preliminary study was conducted on the use of navigation instruments and programs conducted in the previous demonstration and experiment of the training ship SAEBADA, real-time sharing and accuracy of land and sea information, and the development of real-time effective information transmission and reception and management system programs. Based on this, this study used the training ship CHAMBADA, which is similar in length to the tonnage of the fishing boat. The purpose is to present errors in ship situation-specific information data, alarm systems for marine information, and land and ship transmission and reception programs collected using intelligent information management systems to find out whether they can be applied and used in fishing boats. It plans to conduct research on direct and indirect safety and ship’s stability when the intelligent information management system operates in real time in the operation of ships considering the characteristics of small fishing boats and the characteristics of fishing.

The need for an intelligent information-based ship accident prevention and control system for various marine accidents is very clearly emerging. As it is a variety of marine accidents, the causes are diverse. Therefore, it can be said that it is very important to prevent these marine accidents and their causes in advance. Therefore, a study was conducted on an intelligent information-based ship safety management decision support system that provides information necessary for decision-making at sea and land through an integrated management device for ships that informs safety-related risks in real-time ship operation. In the future, we intend to pursue the development of a system that can aim for safer and more economical ship operation by linking it to navigation instruments through the evaluation and analysis of AI, IoT, and big data.