• Ocean Systems Engineering
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• 제9권3호
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• pp.329-347
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• 2019

The shipbuilding industry is characterized by order production, and various processes are performed simultaneously in the construction of ships. Therefore, effective management of the production process and productivity improvement form important key factors in the industry. For decades, researchers and process managers have attempted to improve processes by using business process analysis (BPA). However, conventional BPA is time-consuming, expensive, and mainly based on subjective results generated by employees, which may not always correspond to the actual conditions. This paper proposes a method to improve the production process of offshore plant modules by analysing the process mining data obtained from the shipbuilding industry. Process mining uses information accumulated from the system-provided event logs to generate a process model and determine the values hidden within the process. The discovered process is visualized as a process model. Subsequently, alternatives are proposed by brainstorming problems (such as bottlenecks or idle time) in the process. The results of this study can aid in productivity improvement (idle time or bottleneck reduction in the production process) in conjunction with a six-sigma technique or ERP system. In future, it is necessary to study the standardization of the module production processes and development of the process monitoring system.

• The Journal of Asian Finance, Economics and Business
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• 제9권9호
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• pp.249-261
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• 2022

Globally, COVID-19 has significantly impacted many different organizations and people. From the banks' perspective, this pandemic has affected banks' corporate and retail customers. Also, banks had to adjust to distributed workforce model. This paper analyses the lessons learned from the COVID-19 pandemic, which can be effectively used to rebuild banks' Operational Risk Management capabilities. The present study used the survey research methodology, which includes structured questionnaires completed by senior banking professionals to analyze the learnings from COVID-19 and understand the distributed workforce model and remote working effectiveness. Findings: The Pandemic accelerated the pace of digital transformation. The lockdown imposed due to the pandemic led to employees working remotely, which has been effective because of enhanced digital capabilities. However, enhanced monitoring is required to prevent data-related issues, and action needs to be taken to address challenges faced in having a remote distributed workforce model, like negative impact on on-the-job learning, data-related risks, and employee wellbeing. COVID-19 is an unprecedented event that could not have been predicted in any scenario analysis. This crisis has highlighted various systemic drawbacks that need to be addressed. Banks can apply the lesson learned from this Pandemic to become more robust in the future.

• International Journal of Internet, Broadcasting and Communication
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• 제14권1호
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• pp.122-128
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• 2022

Minimization of human casualties in disaster situations is of paramount importance. In particular, if a marine disaster occurs, it can be directly connected to human casualties, so prompt action is needed. In the event of a marine disaster, the route and location of movement should be identified and life tubes should be used to float on the water. This paper designs and proposes an emergency IoT notification system that can quickly rescue drowning people. The maritime emergency IoT notification system consists of four main types. First, an emergency IoT device that detects the expansion of the life tube and delivers location and situation information to the emergency IoT notification server. Second, an emergency IoT web server that manages emergency information and provides notification. Third, a database server that stores and manages emergency IoT notification information. And finally, an emergency notification app that can receive and respond to emergency notification information. The emergency IoT device consists of a TPMS(Tube Pressure Monitoring System) device that checks the pressure value of the TPMS in real time and sends it to the IoT device, and an IoT device that sends the rescuer's voice information and emergency information to the emergency IoT server. Emergency information is delivered using the MQTT(Message Queuing Telemetry Transport) protocol, and voice information is delivered to the IoT server as HTTP FormData.

• 한국전자통신학회논문지
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• 제18권3호
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• pp.433-440
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• 2023

반사경이 차량에 의해 충돌되는 경우나 폭풍 등으로 돌아가는 경우, 충격 센서에 의해 이벤트가 뜨고 트리거가 작동된다. 본 논문의 트리거 처리 알고리즘은 자이로 센서의 X, Y, Z값을 등록된 값과 비교하여 3축 구동 모터의 작동에 의해 원래의 값으로 구동되도록 제안한다. 그리고 차량번호판을 인식해서 도난이나 사회적인 문제가 되는 차량이면 경찰 작전망에 정보제공 처리한다. 반사경이 도난이나 위치 이동이 발생하는 경우, 등록된 GPS값을 가지고 있기 때문에 도난 모니터링 기능을 작동하여 처리할 수 있도록 한다.

• 천문학회보
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• 제44권2호
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• pp.52.1-52.1
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• 2019

Exploring the vicinity of super-massive black holes (SMBHs) is one of the frontiers in astrophysics. KaVA AGN Science WG has launched its Large Program in 2014 focusing on two SMBHs, Sgr A∗ and M87. They are selected based on their large apparent size. Sgr A∗ is the excellent laboratory for studying gas accretion process onto SMBH and M87 is well known as the best case for investigating plasma outflow ultimately driven by SMBH. For Sgr A∗, KaVA and EAVN provides superb UV-coverage on its emitting region and its scattering medium. In the case of M87, we have conducted high cadence dual-frequency (22and 43GHz )VLBI monitoring to clarify the global profile of the M87 jet velocity field and the spectral index map, which should reflect global structure of magnetic fields in the jet. From 2017, the AGN LP is recognized as multi-wavelength EHT project, conducting quasi-simultaneous coherent observations of M87 and Sgr A∗ with the Event Horizon Telescope (EHT) during its campaign observation periods. AGN WG is reviewing and revising its LP to convert it to EAVN LP. We will briefly report our scientific results and future plan which includes even broader international collaboration, namely East-Asia to Italy Nearly Global (EATING) VLBI to reach higher angular resolution.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.134-134
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• 2022

Streamflow forecasting plays a crucial role in water resource control, especially in highly urbanized areas that are very vulnerable to flooding during heavy rainfall event. In addition to providing the accurate prediction, the evaluation of effects and importance of the input predictors can contribute to water manager. Recently, machine learning techniques have applied their advantages for modeling complex and nonlinear hydrological processes. However, the techniques have not considered properly the importance and uncertainty of the predictor variables. To address these concerns, we applied the GA-BART, that integrates a genetic algorithm (GA) with the Bayesian additive regression tree (BART) model for hourly streamflow forecasting and analyzing input predictors. The Jungrang urban basin was selected as a case study and a database was established based on 39 heavy rainfall events during 2003 and 2020 from the rain gauges and monitoring stations. For the goal of this study, we used a combination of inputs that included the areal rainfall of the subbasins at current time step and previous time steps and water level and streamflow of the stations at time step for multistep-ahead streamflow predictions. An analysis of multiple datasets including different input predictors was performed to define the optimal set for streamflow forecasting. In addition, the GA-BART model could reasonably determine the relative importance of the input variables. The assessment might help water resource managers improve the accuracy of forecasts and early flood warnings in the basin.

• Steel and Composite Structures
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• 제49권4호
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• pp.407-417
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• 2023

After a disaster like the catastrophic earthquake, the government have to use rapid assessment of the condition (or damage) of bridges, buildings and other infrastructures is mandatory for rapid feedbacks, rescue and post-event management. Many domain schemes based on the measured vibration computations, including least squares estimation and neural fuzzy logic control, have been studied and found to be effective for online/offline monitoring of structural damage. Traditional strategies require all external stimulus data (input data) which have been measured available, but this may not be the generalized for all structures. In this article, a new method with unknown inputs (excitations) is provided to identify structural matrix such as stiffness, mass, damping and other nonlinear parts, unknown disturbances for example. An analytical solution is thus constructed and presented because the solution in the existing literature has not been available. The goals of this paper are towards access to adequate, safe and affordable housing and basic services, promotion of inclusive and sustainable urbanization and participation, implementation of sustainable and disaster-resilient buildings, sustainable human settlement planning and manage. Simulation results of linear and nonlinear structures show that the proposed method is able to identify structural parameters and their changes due to damage and unknown excitations. Therefore, the goal is believed to achieved in the near future by the ongoing development of AI and control theory.

• Journal of Plant Biotechnology
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• 제43권1호
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• pp.91-98
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• 2016

면화(cotton)는 섬유를 수확하고, 면실유는 식용으로 가공 후 남은 것은 다시 사료로 이용되어 다방면으로 다양하게 활용되는 작물이다. LM 면화는 옥수수, 대두, 캐놀라와 달리 아시아권(중국, 인도 등)에서 가장 많이 재배되고 있으며, 우리나라는 2013년까지 세계 1위의 LM 면실(사료용) 수입국이며, 세계 4위의 면실박 수입국으로 해마다 증가하는 LM 면화의 수입량과 맞물려 유통 및 소비과정에서의 비의도적으로 유출 가능성이 증가함에 따라 LM 면화의 자연생태계 위해성 평가 및 안전관리가 요구된다. 본 연구에서는 국내 수입 승인 LM 면화 6개 이벤트(MON15985, MON531, GHB614, LLCOTTON25, MON88913, MON1445)의 동시증폭 검출법(multiplex PCR)을 개발하여 보다 신속하고 명확한 검출기법을 확립하고자 하였다. 최적 multiplex PCR 반응 조건은 2개 LM 면화 이벤트 MON15985 (214 bp), MON531 (270 bp)와 4개 LM 면화 이벤트 GHB614 (119 bp), LLCOTTON25 (164 bp), MON88913 (276 bp), MON1445 (389 bp)가 한번의 반응에 명확하게 검출되는 최적 반응 조건 및 primer 반응 농도의 조절을 통해 서로 다른 생성물 크기로 명확히 구분되도록 하였고, 최적 primer 농도는 반응액 최종농도 0.2~0.66 pmol로 primer 쌍 마다 각각 다른 최적 농도 조합의 cocktail을 만들어 활용하였다. Duplex PCR 반응 조건은 초기 $95^{\circ}C$ 5분 반응 후, $95^{\circ}C$ 15초, $55^{\circ}C$ 20초 15회 반응하고, 다시 $95^{\circ}C$ 15초, $60^{\circ}C$ 20초 25회 반응하였을 때 최적 검출이 이루어졌고, tetraplex PCR 반응 조건은 $95^{\circ}C$ 5분 반응 후, $95^{\circ}C$ 15초, $60^{\circ}C$ 20초 50회 반응하였을 때 최적 검출이 이루어졌다. 본 연구에서 개발된 multiplex PCR 검출법은 국내 수입 유통 LM 면화의 자연환경 모니터링에 활용함에 있어 요구되는 연구인력, 시간 및 비용을 보다 효율적으로 개선하는데 적용될 수 있을 것으로 사료된다.

• 지구물리와물리탐사
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• 제19권1호
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• pp.37-44
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• 2016

$CO_2$-EOR ($CO_2$-Enhanced Oil Recovery)은 석유회수증진법 중 하나로, 석유 생산량을 증대시키는 동시에 이산화 탄소를 지중에 격리시킬 수 있는 기술이다. 하지만 이산화탄소 주입 시, 지층 내 균열이 발생하는 경우 저류층 내에 이산화탄소의 영구저장이 어려워지고, 지하수 및 토양의 오염을 야기할 수 있다. 따라서 이 연구에서는 인도네시아 머루압 유전에 이산화탄소 주입 시, 미소진동 모니터링을 수행하여 저류층 내 균열의 발생여부를 파악하고자 하였다. 미소진동 초동 발췌에는 Improved MER (Modified Energy Ratio) 방법을 이용하였다. 초동 발췌 후에는 이벤트의 방위각을 계산하기 위하여 다성분 지오폰에서 기록된 트레이스를 이용해 호도그램 분석을 수행하였다. 최종적으로 초동 발췌 결과와 호도그램 분석 결과를 이용하여 미소진동 위치결정을 수행하였다. 미소진동 위치결정 결과를 통해 저류층 주변에 균열의 발생여부를 확인해 본 결과, 미소진동 발생 위치는 모두 지표에서 나타나고 있으며 저류층 내 균열은 확인되지 않았다. 또한 잡음의 특성을 분석하여 초동 발췌된 이벤트가 대부분 규칙적인 기계적 잡음에 의한 것임을 확인할 수 있었다.

• 환경생물
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• 제29권2호
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• pp.98-106
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• 2011

마산만의 수질변화와 chlorophyll-$a$의 관계를 밝히고자 이 해역을 대표할 수 있는 4개 정점에서 2010년 2월부터 2010년 11월까지 강우와 기온을 포함한 기후학적 요인, 물리 화학적 요인, 그리고 chlorophyll-$a$의 변화를 집중 조사하였다. 그 결과, 수온, 염분, SS, 규산염은 정점별 차이를 보이지 않았으며, COD 및 DIN은 마산만 내만으로 갈수록 증가하였다. 시계열적으로는 여름철 집중 강우 시 마산만 지류 하천 및 낙동강을 통해 담수가 유입되면서 염분의 급강하 및 SS량과 COD의 증가가 나타났다. 영양염류 중 DIN은 여름철 집중강우 시 일시적으로 증가하는 양상을 제외하고는 낮은 농도를 보였고 DIP와 규산염 또한 DIN과 유사한 양상을 보였다. 마산만에서 식물플랑크톤 성장에 영향을 주는 영양염류는 봄철 중반까지 규소와 인이 성장제한인자로 작용하고, 늦봄부터 가을까지는 질소원이 주 성장제한요인으로 작용할 것으로 판단된다. 다중회귀분석을 실시한 결과, 늦겨울부터 봄철까지 chlorophyll-$a$ 농도는 수온, 염분, COD, DIP의 변화에 의해 영향을 받고 있었다. 여름철에는 봄철과 달리, 염분 및 COD, 강수량이 영향을 주고 있어, 여름철 집중강우에 따른 영향인자들에 의해 chlorophyll-$a$가 빠르게 변화됨을 알 수 있었다. 따라서 마산만 해역의 chlorophyll-$a$의 변화는 영양염류와 같은 화학적 인자의 영향과 함께, 수온 및 강수와 같은 물리적 인자에 의해 크게 영향을 받고 있다.