• 한국안전학회지
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• 제36권2호
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• pp.58-67
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• 2021

Incident investigation is regarded as a means to improve safety performance. For the prevention of industrial accidents, measures such as providing safety education, enhancing management interest and participation, establishing a safety management system, and conducting inspection of the work site are necessary. In particular, accident investigation activities, which are an important element of safety management, help to prevent similar accidents, thereby minimizing damage and enhancing work safety. They are critical for understanding business-related incidents and the vulnerabilities and opportunities associated with them. Therefore, it is clear that accident investigation activities are important for accident prevention. The primary focus of many incident investigation processes is on identifying the cause of an event. While considerable research has been conducted on potential accident investigation tools there has been little research on including the views and experiences of practitioners in the accident investigation process. In this study, a questionnaire survey was conducted among safety managers in the domestic manufacturing/construction industry to understand the practice of accident investigation. The investigation pertained to companies' accident investigation systems, the competence of investigators, and the identification and recommendations of the cause of accidents. From the analysis results of accident investigations, investigators' competence, the difficulty level of investigations, and the root causes of accidents were identified from the viewpoint of the participants of the accident investigations. In particular, the development of standardized and simple accident investigation methods and their dissemination to companies were found to be necessary for activating the root cause of accidents. Based on this, it can be used as basic data for the development of root cause analysis investigation techniques that are easily applicable to organizations.

• 드라이브 ㆍ 컨트롤
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• 제17권4호
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• pp.125-132
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• 2020

The objective of this study was to analyze the axle load of a rice transplanter when planting rice seedlings at different working load conditions to select a suitable gear stage and a constant planting depth for rice seedlings. In this study, there are four levels of planting distances (26, 35, 43, and 80 cm) and three planting depths (low, medium, and high) with two gear stages (1.3 and 1.7 m/s). Axle loads and required planting pressures were analyzed statistically. It was observed that axle torques were increased with increasing planting depths for both gear stages, meaning that axle torques were directly proportional to planting depths for both gear stages. It was also observed that required planting pressures had a significant difference between planting distances. Planting pressures also showed significant difference according to gear stage and planting depth. These results indicate that planting pressures were directly proportional to both gear stage and planting depth. Results revealed that the automatic depth control system of a rice transplanter could not guarantee a constant planting depth as supplied pressures were variable. This indicates that a control algorithm is needed to ensure a constant planting depth. In the future, a control algorithm will be developed for an automatic depth control system of a rice transplanter to improve its comprehensive performance and efficiency.

• 한국컴퓨터정보학회논문지
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• 제26권2호
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• pp.27-37
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• 2021

본 논문에서는 관로를 자동으로 검출하는 지하 관로 자동 탐색 시스템을 제안한다. 시간에 따른 지반변화, 관로 시공 불일치 등 여러 가지 요인으로 실제 관로의 위치가 지하 관로 도면과 일치하지 않는다. 이로 인하여 굴착공사나 관로 노후화에 의한 여러 사고가 발생한다. 사고를 방지하기 위해 GPR(지표 투과 레이더, Ground Penetrating Radar) 탐사를 통해 지하시설물을 찾아내는 작업이 이루어지고 있지만, 분석을 담당할 수 있는 전문가의 수가 부족하다. GPR 데이터는 매우 방대하며 분석과정에도 오랜 시간이 걸리기 때문이다. 이에 본 논문에서는 3D GPR 데이터를 자동으로 분석하기 위해 딥 러닝 기술인 3D 이미지 분할을 사용하고, 이에 적합한 데이터 생성 알고리즘을 제안한다. 또한 GPR 데이터 특성에 맞는 데이터 증강 기법, 데이터 전처리 모듈을 제안한다. 실험 결과를 통해 제안한 시스템은 F1 Score 40.4%의 성능을 보였으며 이를 통해 이미지 분할을 이용한 관로 분석의 가능성을 확인하였다.

• 한국산학기술학회논문지
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• 제22권3호
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• pp.88-93
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• 2021

선박의 건조과정이나 압력용기의 몸체 부분을 용접할 경우 많이 사용되는 SAW(Submerged Arc Welding)는, 용접 부위에 분말 형태의 용제(Flux)를 일정 두께로 살포하고 그 속에 전극 와이어를 연속적으로 공급하여 용접이 이루어지는 방법으로 용접 시 발생되는 아크열이 외부로 노출되지 않는 특징이 있으며, 잠호용접으로 불리기도 한다. 또한 1,500~3,000A의 전류까지 통전 할 수 있는 고전류 용접이 가능하며 아크효율이 95% 이상, 미세한 금속 산화물 입자인 Welding Fume 발생량이 적고 아크광선이 외부로 노출되지 않아 청정한 작업이 가능하며 용입이 깊고 결함이 잘 발생하지 않아 용접 이음부의 신뢰도가 높다. 본 연구에서는 SM490C-TMC 후판을 서브머지드 아크용접(SAW)을 이용하여 이종 용접한 후 용접부의 기계적 특성을 인장, 경도, 매크로, 자분탐상 검사 결과를 분석하여 다음과 같은 결론을 도출하였다. 굽힘 시험 결과 시료에서 표면의 터짐 현장이 발생하지 않았고 기타 결점의 유무를 확인할 수 없었으며, 이는 용접 이후 소성변형 과정에서도 충분한 인성을 발휘하고 있는 것으로 나타났으며, 1F 용접 방법이 굽힘 성능에 문제가 없는 것으로 판단되었다.

• Advances in concrete construction
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• 제11권1호
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• pp.81-88
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• 2021

Rice Husk Ash (RHA) geopolymer paste activated by sodium aluminate were characterized by X-ray diffractogram (XRD), scanning electron microscope (SEM), energy dispersion X-Ray analysis (EDAX)and fourier transform infrared spectroscopy (FTIR). Five series of RHA geopolymer specimens were prepared by varying the Si/Al ratio as 1.5, 2.0, 2.5, 3.0 and 3.5. The paper focuses on the correlation of microstructure with hardened state parameters like bulk density, apparent porosity, sorptivity, water absorption and compressive strength. XRD analysis peaks indicates quartz, cristobalite and gibbsite for raw RHA and new peaks corresponding to Zeolite A in geopolymer specimens. In general, SEM micrographs show interconnected pores and loosely packed geopolymer matrix except for specimens made with Si/Al of 2.0 which exhibited comparatively better matrix. Incorporation of Al from sodium aluminate were confirmed with the stretching and bending vibration of Si-O-Si and O-Si-O observations from the FTIR analysis of geopolymer specimen. The dense microstructure of SA2.0 correlate into better performance in terms of 28 days maximum compressive strength of 16.96 MPa and minimum for porosity, absorption and sorptivity among the specimens. However, due to the higher water demand to make the paste workable, the value of porosity, absorption and sorptivity were reportedly higher as compared with other geopolymer systems. Correlation regression equations were proposed to validate the interrelation between physical parameters and mechanical strength. RHA geopolymer shows comparatively lower compressive strength as compared to Fly ash geopolymer.

• 한국정보통신학회논문지
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• 제24권8호
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• pp.1102-1108
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• 2020

이동 엣지 컴퓨팅은 중앙 집중식 데이터 처리가 아닌 데이터가 생성되는 네트워크의 에지와 가까운 곳에서 데이터를 처리하는 방식으로 클라우드 컴퓨팅의 단점을 보완하여 새로운 전기를 마련할 수 있는 기술이다. 데이터를 처리하고 연산하는 곳을 따로 먼 데이터 센터에 두는 것이 아닌, 이동 단말 장치들과 가까운 엣지에 컴퓨팅 능력을 부가하고 데이터 분석까지 가능하게 하여 저지연/초고속 컴퓨팅 서비스의 실현이 가능하게 하였다. 본 논문에서는 EdgeCloudSim 시뮬레이터를 이용해 클라우드와 엣지 노드가 협업하여 이동 단말의 컴퓨팅 작업 처리를 분업화 하는 가상의 이동 엣지 컴퓨팅 테스트베드 환경을 개발한다. 개발된 가상 이동 엣지 컴퓨팅 테스트베드 환경은 중앙 클라우드와 엣지 컴퓨팅 노드들 사이에서 이동 단말들의 컴퓨팅 작업 분배를 위한 오프로딩 기법들의 성능을 평가하고 분석한다. 가상 이동 엣지 컴퓨팅 테스트베드 환경 및 오프로딩 성능 평가를 제시함으로써 클라우드와 협업하는 이동 엣지 컴퓨팅 노드 구축을 준비하는 산업계 엔지니어들에게 하나의 사전 지식을 제공하고자 한다.

• 디지털산업정보학회논문지
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• 제17권1호
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• pp.57-73
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• 2021

The purpose of this study is to analyze the diffusion period and productivity of smartwork in an organization. Firms are increasingly interested in smartwork for non contact work and working from home because of the corona 19. The smartwork is a new technology that changes face-to-face work in an organization. It helps the work of individuals and organizations regardless of time and place. The theoretical background describes the complexity, system thinking, diffusion theory, smart work, organizational resistance, and productivity. This study analyzes the diffusion period and productivity of smart work through business simulation techniques. A simulation study progresses four stages. There are problem definition, hypothesis establishment and causal loop diagram, model construction and verification, and policy evaluation. The simulation models contain an individual's resistance variables organizational investment and leadership variables related to the operation of smartwork. The organizational investment variables include organizational culture, legal system, implement systems and technology investment. The individual resistance variables include cognitive, attitude, structure and technological resistance. The leadership includes leadership interest variables and performance linkage variables. The simulation executed the changes of a people number adopting smart work and the organizational productivity monthly. As a result of the simulation, many organization members have accepted the smart work innovation after 20 months. The organizational productivity through smart work showed very high value after 16 months. In scenario analysis, the individuals' awareness and attitude resistance showed very important variables to productivity and a personal change of smart work adoption. Meanwhile, The organizational investment showed that the high driving-force increased not productivity and the low driving-force showed decreased low productivity. Also, leadership variables showed a powerful driver for changing smart work productivity. The implication of the study has suggested extending complexity, diffusion theory and organization resistance theory based on simulation methods.

• 한국구조물진단유지관리공학회 논문집
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• 제25권1호
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• pp.103-111
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• 2021

보물 제49호 나주 석당간은 성능 저하로 인해 해체 및 복원 공사가 이루어 졌다. 공사시 균열 부위가 보강되고 기울어짐이 개선되었다. 이러한 문화재들의 복원 공사 전·후의 강성 변화를 정량적으로 분석하고 데이터베이스로 구축하여 보강 효과에 대한 예측 또는 평가를 할 수 있는 과학적인 비파괴 검사 방법에 대한 연구가 필요하다. 복원 공사 전·후에 진동실험에 의해 측정된 고유진동수와 탄성계수 정보로부터 구조 시스템의 전체적인 강성을 추정할 수 있는 단순 식을 유도하였고, 활용성을 검토하였다. 제시된 방법으로 중요 문화재의 강성을 정기적으로 조사한다면 구조안전진단 필요 시점 또는 보수, 보강의 필요 시점을 추정하는 자료로 활용될 수 있을 것을 판단된다.

• 한국농공학회논문집
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• 제64권1호
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• pp.39-50
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• 2022

In this study, we suggested the optimal training period for predicting the streamflow using the LSTM (Long Short-Term Memory) model based on the deep learning and CMIP5 (The fifth phase of the Couple Model Intercomparison Project) future climate scenarios. To validate the model performance of LSTM, the Jinan-gun (Seongsan-ri) site was selected in this study. We comfirmed that the LSTM-based streamflow was highly comparable to the measurements during the calibration (2000 to 2002/2014 to 2015) and validation (2003 to 2005/2016 to 2017) periods. Additionally, we compared the LSTM-based streamflow to the SWAT-based output during the calibration (2000~2015) and validation (2016~2019) periods. The results supported that the LSTM model also performed well in simulating streamflow during the long-term period, although small uncertainties exist. Then the SWAT-based daily streamflow was forecasted using the CMIP5 climate scenario forcing data in 2011~2100. We tested and determined the optimal training period for the LSTM model by comparing the LSTM-/SWAT-based streamflow with various scenarios. Note that the SWAT-based streamflow values were assumed as the observation because of no measurements in future (2011~2100). Our results showed that the LSTM-based streamflow was similar to the SWAT-based streamflow when the training data over the 30 years were used. These findings indicated that training periods more than 30 years were required to obtain LSTM-based reliable streamflow forecasts using climate change scenarios.

• Design & Manufacturing
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• 제15권4호
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• pp.24-31
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• 2021

In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection molding conditions and the weight, height, and diameter of the product derived from CAE results were used as the input parameters for learning of the convert model. Also the product qualities of experimental results were used as the output parameters for learning of the convert model. The accuracy of the convert model showed RMSE values of 0.06g, 0.03mm, and 0.03mm in weight, height, and diameter, respectively. As the next step, additional randomly selected conditions were created and CAE analysis was performed. Then, the additional CAE analysis data were converted to similar experimental data through the conversion model. An artificial neural network model was constructed to predict the quality of injection molded product by using converted similar experimental data and injection molding experiment data. The injection molding conditions were used as input parameters for learning of the predicted model and weight, height, and diameter of the product were used as output parameters for learning. As a result of evaluating the performance of the prediction model, the predicted weight, height, and diameter showed RMSE values of 0.11g, 0.03mm, and 0.05mm and in terms of quality criteria of the target product, all of them showed accurate results satisfying the criteria range.