부유식 해상태양광 설비는 패널 지지를 위한 프레임 구조물, 구조체의 부력 제공을 위한 부유체와 전체 시스템의 거동을 제한하는 계류시설로 구성되어 있다. 계류시설은 구조물의 지지조건으로서의 역 할을 통해 안정적인 발전량 수급에 기여한다. 하지만 해당 시스템은 설치된 해상환경 특성상 계류선의 파단 및 손상 시 직접적인 탐지가 불가능해 유지관리에 어려움이 있다. 따라서 본 연구에서는 패널지 지 프레임 구조체에 가속도 센서 부착을 가정하여, 해당 센서 계측값을 토대로 계류설비에서 발생한 파단 및 손상이 발생한 위치를 추정하는 알고리즘을 개발하였다. 알고리즘은 비지도학습 인공지능의 일종인 오토인코더를 활용하여 가속도 계측값의 재현 과정을 통해 정상상태의 구조 응답을 학습한 모 델이 비정상상태의 계측값을 재현 시 발생한 오차를 통해 손상 발생 여부와 위치를 실시간 탐지하도 록 구성하였다. 정상상태 구조응답을 기반으로 한 모델의 학습을 위해 패널지지 구조체를 10x10 격자 형으로 결합한 다중 결합 시스템에 불규칙파랑을 환경하중으로 적용함을 통해 발생한 6자유도 가속도 데이터를 확보하였다. 계류시설의 손상 발생 시 주된 변화 인자 탐지를 위해 상관성 분석과 민감도 분 석을 실시하여 손상 위치 추정 알고리즘에 적용할 주요 인자를 선별하여 학습 및 추정 성능에 대한 비교 분석을 수행하였다. 구축된 알고리즘의 테스트를 위해 총 5개 종 손상 케이스 데이터셋을 구축하 여 손상 위치 추정 성능을 비교하였다. 본 연구를 통해 계류 시설에 발생한 손상 여부 및 위치를 추정 하여 부유식 해상태양광 설비의 선제적 유지관리에 기여할 수 있을 것으로 기대된다.

Considering the difficulties of the manufacturing industry by improving production efficiency in the era of high wages and aging in domestic automation facilities, automation facilities are considered an irreversible trend, but many serious related disasters are occurring due to the problems of increasing automation facilities due to the enlargement of manufacturing processes, line-up, and automation. The purpose of this study is to review the usage conditions and safety measures for industrial robots that are experiencing serious industrial accidents and are expected to continue to increase in facilities among automation facilities at the automation industrial site and propose ways to ensure the fundamental safety of the facilities at all times The suggestions are as follows. The purpose is to prevent safety accidents in advance by applying safety door aids to industrial sites and installing additional safety devices in safety slide door lock systems applied to safety fence doors of new and already installed facilities to detach safety keys and ensure that workers carry them at all times.

PURPOSES : Carbon dioxide (CO2) is a cheaper and easier to installer fire suppressant than other extinguishing gases and is easy to install, but extinguishes fires by is suffocation. As a result, suffocation accidents continue to occur in facilities equipped with CO2 fire-extinguishing facilities, Emission standards have yet to be established. This study aims to address the issue. METHODS : To effectively remove CO2 emitted from installed systems, we reviewed and analyzed previous related research and existing international standards. RESULTS : In protected areas where CO2 fire-extinguishing systems are installed, emission facilities should discharge the emitted CO2 before it enters the protected spaces. CO2 sensors can determine whether safe entry is possible or whether respirators are required. CONCLUSIONS : This study presented a specific installation method for emission facilities capable of actively discharging CO2. Applying this method is expected to contribute to improving safety in facilities equipped with CO2 fire-extinguishing facilities.

PURPOSES : This study aimed to secure the work space in alarm valve rooms to ensure stable working conditions for the engineers. METHODS : We analyzed situations where alarm valve room work spaces were inadequately secured posing a problem. Using the Ovako working posture analysis system (OWAS) method, we measured the body size of adult male technicians and their tools to analyze the actual space needed for them to maintain a healthy posture while working. On this basis, we proposed regulations governing workspace size and acceptable durations for unstable body postures. RESULTS : By measuring valve room work space and technician body size, we found that the workspace was inadequate for technicians. Applying OWAS showed that securing more space in the valve room improved the construction stability. CONCLUSIONS : Regulations on valve room size and appropriate work space for technicians will improve construction and inspection stability. This reduces the probability of poor construction and inadequate inspection, increasing the reliability of the firefighting facility system.

By developing molds and facilities to horizontally mold the functional part of the dry-cast concrete block, We intend to develop molds and a series of facilities to horizontally mold the functional part of the dry-cast concrete block to increase production per cycle while maintaining existing production methods and major facilities. In order to do so, CAE analysis is first required to develop molds and facilities for horizontally molding the functional part of the drycast concrete block in the horizontal direction. The procedure will be carried out by reviewing the validity of boundary conditions and physical properties, 3D modeling, grid generation, construction of analysis models, model validity, analysis according to frequency changes, and analysis according to physical properties. First, through the comparison of two-point support, three-point support, and two-point and three-point support in the constraint conditions, We would like to compare it with the actual molded product in the horizontal direction. But first of all, it is considered two-point support in the constraint conditions in this paper.

MCCI, one of the ex-vessel phenomena during a severe accident, is generally caused by mutual reactions between molten core and reactor building basemat concrete, but occurs between sacrificial materials made by concrete and corium in APR nuclear power plant with PECS applied. In this paper, MCCI analysis was performed to design and apply a device to block the core melt at the junction connected to the ICI cavity sump from the core catcher installed in the reactor cavity of the APR. Unlike the sacrificial material, which has a cooling effect by inducing erosion of the ex-vessel core melt, the device is composed of concrete applied as a barrier to protect the ICI cavity sump. The decent thickness of the barrier wall of the device was calculated.

This paper proposes an algorithm for the Unrelated Parallel Machine Scheduling Problem(UPMSP) without setup times, aiming to minimize total tardiness. As an NP-hard problem, the UPMSP is hard to get an optimal solution. Consequently, practical scenarios are solved by relying on operator's experiences or simple heuristic approaches. The proposed algorithm has adapted two methods: a policy network method, based on Transformer to compute the correlation between individual jobs and machines, and another method to train the network with a reinforcement learning algorithm based on the REINFORCE with Baseline algorithm. The proposed algorithm was evaluated on randomly generated problems and the results were compared with those obtained using CPLEX, as well as three scheduling algorithms. This paper confirms that the proposed algorithm outperforms the comparison algorithms, as evidenced by the test results.

Non-structural elements, such as equipment, are typically affixed to a building’s floor or ceiling and move in tandem with the structure during an earthquake. Seismic forces acting upon non-structural elements traverse the ground and the building’s structure. Considering this seismic load transmission mechanism, it becomes imperative to account for the interactions between soil, structure, and equipment, establishing seismic design procedures accordingly. In this study, a Soil-Structure-Equipment Interaction (SSEI) model is developed. Through seismic response analysis using this model, how the presence or absence of SSEI impacts equipment behavior is examined. Neglecting the SSEI aspect when assessing equipment responses results in an overly conservative evaluation of its seismic response. This emphasizes the necessity of proposing an analytical model and design methodology that adequately incorporate the interaction effect. Doing so enables the calculation of rational seismic forces and facilitates the seismic design of non-structural elements.

본 연구에서는 소형선박용 중·고속 디젤엔진에 적용하여 연구 중인 SCR+DPF 기술을 저속엔진이 설치된 선박에 탑재하여 해상 실증 시험을 수행하였다. 대상 선박(총 톤수 2,881 톤, 정격출력 1,470kW@240rpm ×1)은 국내 연해를 운항하는 일반화물선으로 배출저감설 비의 선박 탑재를 위해 도면 개발, 승인 및 선박 임시검사를 수행하였다. 저감성능 확인을 위해서 가스상물질 측정장비는 NOx technical code 및 ISO-8178의 분석방법을 준용하는 장비를 사용하였으며, 입자상물질 측정장비는 국제해사기구(IMO)에서 논의하고 있는 블랙카본 측정 방법 중 하나인 스모크미터를 사용하였다. 시험은 황 함유량 따라 MGO(0.043%), LSFO(0.42%) 2종의 연료를 사용하였으며 실제 운항 하는 엔진회전수(130, 160 및 180 rpm)를 고려하여 시험 조건을 설정하였다. 시험 조건에 따라 배출저감설비의 전·후단에서 가스상 및 입 자상(매연) 물질을 측정하여 배출저감설비의 저감효율을 확인하였으며 모든 시험 조건에서 NOx의 경우 90% 이상, 입자상물질(매연)의 경우 95% 이상의 저감효율을 확인하였고 엔진 성능의 영향을 줄 수 있는 배기가스 압력은 허용배압 기준인 50mbar 이하를 만족하였다. 본 연구를 통해 해상실증 연구의 중요성과 중소형 저속엔진 선박의 질소산화물 및 입자상물질의 동시 저감을 위한 대응 기술로 SCR+DPF 설비 적용 가능성을 확인할 수 있었다.

This paper is proposing a novel machine scheduling model for the unrelated parallel machine scheduling problem without setup times to minimize the total completion time, also known as “makespan”. This problem is a NP-complete problem, and to date, most approaches for real-life situations are based on the operator’s experience or simple heuristics. The new model based on the Memetic Algorithm, which was proposed by P. Moscato in 1989, is a hybrid algorithm that includes genetic algorithm and local search optimization. The new model is tested on randomly generated datasets, and is compared to optimal solution, and four scheduling models; three rule-based heuristic algorithms, and a genetic algorithm based scheduling model from literature; the test results show that the new model performed better than scheduling models from literature.

In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.

The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.

Thin-film shape technology is recognized for its core technology to enhance the technology of LCD, PDP, semiconductor manufacturing processes, hard disks and optical disks, and is widely used to form coated thin films of products. In addition, resistance (electron beam filament) technology for heating is used to manufacture filament for ion implants used in semiconductor manufacturing processes. By establishing an electronic beam filament production system and developing seven specifications of electronic beam filament, it is contributing to improving trade dynamics and increasing exports to Japan through localized media of theoretical imports to domestic companies. In this study, CAE analysis was performed after setting electron beam filament specification and development objectives, facilities and fabrication for electron beam filament production, electron beam filament JIG & fixture design and fabrication followed by electron beam filament prototype. Then, the automation and complete inspection equipment of the previously developed electronic beam filament manufacturing facilities was developed and researched to mass-produce them, to analyze and modify prototypes, design and manufacture automation facilities, and finally, to design and manufacture the complete inspection equipment. In this paper, design and manufacture of electronic beam filament automation facilities for mass production were dealted with.