Damage to gas and fire protection piping systems can lead to secondary disasters after an earthquake, so their seismic design is crucial. Accordingly, various types of seismic restraint installations are being devised, and a new suspended piping trapeze restraint installation has also recently been developed in Korea. In this study, a cyclic loading test was performed on the developed trapeze support system, and its performance was evaluated according to ASHRAE 171, the standard for seismic and wind restraint design established by the American Society of Refrigeration and Air Conditioning Engineers (ASHRAE). The three support system specimens did not break or fracture, causing only insignificant deformations until the end of the experiment. Based on the experimentally rated strength and displacement performance, this trapeze support system is expected to control the seismic movement of piping during an earthquake.

As the importance of artificial intelligence grows rapidly and emerges as a leader in technology, it is becoming an important variable in the next-generation industrial system along with the robot industry. In this study, a safety system was developed using deep learning technology to provide worker safety in a robot workplace environment. The implemented safety system has multiple cameras installed with various viewing directions to avoid blind spots caused by interference. Workers in various scenario situations were detected, and appropriate robot response scenarios were implemented according to the worker's risk level through IO communication. For human detection, the YOLO algorithm, which is widely used in object detection, was used, and a separate robot class was added and learned to compensate for the problem of misrecognizing the robot as a human. The performance of the implemented system was evaluated by operator detection performance by applying various operator scenarios, and it was confirmed that the safety system operated stably.

The advent of big data has brought about the need for analytics. Natural language processing (NLP), a field of big data, has received a lot of attention. Topic modeling among NLP is widely applied to identify key topics in various academic journals. The Korean Society of Industrial and Systems Engineering (KSIE) has published academic journals since 1978. To enhance its status, it is imperative to recognize the diversity of research domains. We have already discovered eight major research topics for papers published by KSIE from 1978 to 1999. As a follow-up study, we aim to identify major topics of research papers published in KSIE from 2000 to 2022. We performed topic modeling on 1,742 research papers during this period by using LDA and BERTopic which has recently attracted attention. BERTopic outperformed LDA by providing a set of coherent topic keywords that can effectively distinguish 36 topics found out this study. In terms of visualization techniques, pyLDAvis presented better two-dimensional scatter plots for the intertopic distance map than BERTopic. However, BERTopic provided much more diverse visualization methods to explore the relevance of 36 topics. BERTopic was also able to classify hot and cold topics by presenting ‘topic over time’ graphs that can identify topic trends over time.

Although the proportion of coal-fired power generation is decreasing, efficient operating technology is needed to continuously invest in facilities and reduce maintenance costs until it is abolished. Boilers, one of the main facilities of power plants, operate for a long time in harsh environments of high temperature and high pressure. In addition, damage due to deterioration is likely to occur depending on the fuel and tube material used. It is very important to judge soundness because damage caused by deterioration adversely affects facility operation. Previously, replication method was used to analyze the progress of deterioration. In the replication method, pre-treatment such as chemical treatment is performed on the boiler tube in the field, the area is reproduced by attaching a film, and the replicated film is determined by an expert in the laboratory with an expensive microscope. However, this method involves substantial costs and time requirements, as well as the possibility of human errors. To address these issues, we developed a mobile health assessment system in this research. Since it is detachable and takes images in real time, this system enables swift evaluations across a broad range and facilitates the assessment of preprocessing quality. In addition, it was intended to reduce existing human mistakes by developing a degradation classification algorithm using the merger cluster method.

In this study, a smart bench was developed and researched smart benches that can contribute to user convenience and satisfaction by installing them in parks, bus stops, and tourist attractions in line with the rapidly changing construction of smart cities. The smart bench is automatically operated by the control system according to the external temperature and provides additional functions such as charging, lighting, and advertising to improve general bench functions as well as heating in winter and cooling in summer, making it suitable for smart urbanization. The developed smart bench is designed to be strong enough to withstand loads of about 2,500 N. It minimizes the visible parts such as assembled bolts and 220V power supply wires, It can also give aesthetic effects. The development was carried out with the aim of waterproofing and dustproofing of IP44 grade in accordance with the climate of Republic of Korea, which has four seasons, and it is advantageous for long-term use because the paint was selected for the weather ability (discoloration) grade 3 or higher. If smart bench is commercialized, it is believed that various options can be provided to the smart bench market, where buyers had few product options, as the parts were developed in an assembled type so that all functions can be responded in an optional form according to the installation environment and the buyer's budget.

In this study, we proposed a simulator for the development of a digital multi-process welding machine and a welding process monitoring system. The simulator, which mimics the data generation process of the welding machine, is composed of process control circuit, peripheral device circuit, and wireless communication circuit. Utilizing this simulator, we aimed to develop a welding process monitoring system that can monitor the welding situations of four multi-process welding machines and three processes each, with data transmission through wireless communication. Through the operation of the proposed simulator, sequential digital processing of multi-process welding data and wireless communication were achieved. The welding process monitoring system enabled real-time monitoring and accumulation of the process data. The selection of upper and lower limits for process variables was carried out using a deep neural network based on allowable changes in bead shape, enabling the management of welding quality by applying a process control technique based on the trend of received data.

Recent, there has been a need for a terminal device with artificial intelligence to detect emergency situations in various means of transportation to prevent overdischarge of the battery even when parking the vehicle for a long time so that the driver can properly prepare. The transmission system, which guides the situation and location information of the vehicle, uses AHD video technology and RTOS technology to reduce the load of wireless networks by increasing the video playback rate through UDP implementation by WiFi, and stabilizes through wireless networks and low-power sensors using LoRa services.

This study aims to prepare a colloidal silica-containing powder to enhance the solubility and dissolution rate of rivaroxaban using a self-nanoemulsifying drug delivery system (SNEDDS). We investigate the impact of colloidal silica on a nanoemulsion system for preparing powdered SNEDDS. The liquid SNEDDS comprises 30/20/50 (w/w/w) Peceol/ Cremophor RH40/Tween 80, which results in the formation of the smallest droplets. Three powdered SNEDDS formulations are prepared by suspending the liquid SNEDDS formulation using colloidal silica and spray drying. The powdered SNEDDS prepared with liquid SNEDDS and colloidal silica at a ratio of 1/0.5 (w/w) exhibits the highest water solubility (0.94 ± 0.62 vs. 26.70 ± 1.81 μg/mL) and dissolution rate (38.4 ± 3.6 vs. 85.5 ± 3.4%, 45 min) when compared to the drug alone. Morphologically, the liquid SNEDDS is adsorbed onto colloidal silica and forms smaller particles. In conclusion, an SNEDDS containing rivaroxaban, prepared using colloidal silica, facilitates the creation of a nanoemulsion and enhances the water solubility of rivaroxaban. Accordingly, this technology holds significant potential for commercialization.

원자력발전소 지진 확률론적 안전성 평가인 PSA(Probabilistic Safety Assessment)는 오랜 기간에 걸쳐 확고히 구축되어 왔다. 반면 에 다양한 공정 기반의 산업시설물의 경우 화재, 폭발, 확산(유출) 재난에 대해 주로 연구되어 왔으며, 지진에 대해서는 상대적으로 연 구가 미미하였다. 하지만, 플랜트 설계 당시와 달리 해당 부지가 지진 영향권에 들어갈 경우 지진 PSA 수행은 필수적이다. 지진 PSA 를 수행하기 위해서는 확률론적 지진 재해도 해석(Probabilistic Seismic Hazard Analysis), 사건수목 해석(Event Tree Analysis), 고장수 목 해석(Fault Tree Analysis), 취약도 곡선 등을 필요로 한다. 원자력 발전소의 경우 노심 손상 방지라는 최우선 목표에 따라 많은 사고 시나리오 분석을 통해 사건수목이 구축되었지만, 산업시설물의 경우 공정의 다양성과 최우선 손상 방지 핵심설비의 부재로 인해 일 반적인 사건수목 구축이 어렵다. 따라서, 본 연구에서는 산업시설물 지진 PSA를 수행하기 위해 고장수목을 바탕으로 확률론적 시각 도구인 베이지안 네트워크(Bayesian Network, BN)로 변환하여 리스크를 평가하는 방법을 제안한다. 제안된 방법을 이용하여 임의로 생성된 가스플랜트 Plot Plan에 대해 최종 BN을 구축하고, 다양한 사건 경우에 대한 효용성있는 의사결정과정을 보임으로써 그 우수 성을 확인하였다.

프리캐스트 코핑의 중공부 주철근 단절로 인한 단점을 보완하고, 거치대 삽입 없이 주철근을 거치대로 활용할 수 있 도록 철근-콘크리트 접촉부의 응력집중을 완화할 수 있는 하중분산세트의 성능을 검토하였다. 유한요소해석 및 축소모형실험을 통해 검토한 결과 하중분산세트는 철근-콘크리트 접촉부의 응력집중을 효과적으로 완화시켜 거치 시 콘크리트 파손을 방지할 수 있을 것으로 판단된다.

PURPOSES : In this study, we aim to broaden the understanding of the factors influencing the accuracy of WIM systems for overload enforcement. Particularly, we explored the proportions and causes of secondary influencing factors (driving path, vehicle class, and acceleration), which have been relatively less studied and reduced the accuracy of the WIM system. METHODS : Overload enforcement data were recorded by the WIM system, and enforcement officers were gathered. The ratios of each data point, which are the relative errors, are used to estimate the accuracy of the WIM system. These relative errors were classified into four driving-path groups, four vehicle-class groups, and three acceleration groups. The change in the accuracy of the WIM system caused by each influencing factor was analyzed by comparing the difference in the average relative error between the classified groups. Analysis of variance (ANOVA) and Welch's ANOVA were used to determine significant differences between groups. RESULTS : Vehicles departing from a normal driving path make it difficult for the GVW compensation algorithm of the WIM system to operate properly. For these abnormal paths, the standard deviation of the average GVW relative error was 22%. There was no specific trend in the difference in accuracy by vehicle class. However, we found that the rear axle and retractable axle were the main causes of the reduced GVW accuracy in each vehicle class. The average GVW relative error remained the same regardless of the acceleration, but the average FAW relative error of the accelerated vehicle was approximately 2.5% lower than that of the unaccelerated vehicle. CONCLUSIONS : An abnormal driving path, lifting of a retractable axle, and rapid acceleration (or deceleration) reduce the accuracy of WIM systems. Intelligent transportation systems, such as traffic signals, telematics devices, and applications that induce desirable driving are required for effective overload enforcement. Additionally, it is necessary to smoothen the road pavement to minimize the dynamic effects on the rear axle.

본 연구는 DFT 재배 시스템을 이용한 밀폐형 식물생산시스 템에 MB처리 유무에 따른 상추(Lactuca sativa L.)와 배초향 (Agastache rugosa Kuntze)의 생육과 양액 특성을 조사하기 위해 수행되었다. 상추와 배초향은 밀폐형 식물생산시스템에 서 23일간 재배되었다. MB는 매일 9시, 13시, 17시에 5분씩 16일간 처리하였다. 상추와 배초향의 엽장, 엽폭, 엽면적, 생 체중 및 건물중은 MB처리구에서 대조구에 비해 유의하게 낮 았다. 상추와 배초향의 총 뿌리길이, 뿌리 표면적, 근단 수도 MB처리구에서 유의적으로 낮게 나타났다. 평균 뿌리 직경의 경우, 상추는 처리 간에 차이가 없었으나, 배초향은 MB처리 구에서 유의적으로 굵어 종 간에 차이가 있음을 확인하였다. 본 연구 결과, DFT 방식으로 상추와 배초향을 재배할 때 MB 처리는 비생물적 스트레스를 유발하여 생육을 억제하는 것으 로 나타났다.

수소는 다양한 신재생에너지 중 환경친화적인 에너지로 각광받고 있지만 농업에 적용된 사례는 드물다. 본 연구는 수소 연료전지 삼중 열병합 시스템을 온실에 적용하여 에너지를 절 약하고 온실가스를 줄이고자 한다. 이 시스템은 배출된 열을 회수하면서 수소로부터 난방, 냉각 및 전기를 생산할 수 있다. 수소 연료 전지 삼중 열 병합 시스템을 온실에 적용하기 위해 서는 온실의 냉난방 부하 분석이 필요하다. 이를 위해서는 온 실의 형태, 냉난방 시스템, 작물 등을 고려해야 한다. 따라서 본 연구에서는 건물 에너지 시뮬레이션(BES)을 활용하여 냉 난방 부하를 추정하고자 한다. 전주지역의 토마토를 재배하 는 반밀폐형 온실을 대상으로 2012년부터 2021년까지의 기 상데이터를 수집하여 분석했다. 온실 설계도를 참고하여 피 복재와 골조를 모델화하여 작물 에너지와 토양 에너지 교환을 실시했다. 건물 에너지 시뮬레이션의 유효성을 검증하기 위 해 작물의 유무에 의한 분석, 정적 에너지 및 동적 에너지 분석 을 실시했다. 또한 월별 최대 냉난방 부하 분석에 의해 평균 최 대 난방 용량 449,578kJ·h-1, 냉방 용량 431,187kJ·h-1이 산정 되었다.

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.