• 수산해양기술연구
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• 제59권1호
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• pp.55-64
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• 2023

Gas turbine engines are widely used as prime movers of generator and propulsion system in warships. This study addresses the problem of designing a DS-based PID controller for speed control of the LM-2500 gas turbine engine used for propulsion in warships. To this end, we first derive a dynamic model of the LM-2500 using actual sea trail data. Next, the PRC (process reaction curve) method is used to approximate the first-order plus time delay (FOPTD) model, and the DS-based PID controller design technique is proposed according to approximation of the time delay term. The proposed controller conducts set-point tracking simulation using MATLAB (2016b), and evaluates and compares the performance index with the existing control methods. As a result of simulation at each operating point, the proposed controller showed the smallest in %OS, which means that the rpm does not change rapidly. In addition, IAE and IAC were also the smallest, showing the best result in error performance and controller effort.

• 대기
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• 제32권1호
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• pp.27-37
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• 2022

The Korean Integrated Model (KIM) forecast system was extended to assimilate Horizontal Line-Of-Sight (HLOS) wind observations from the Atmospheric Laser Doppler Instrument (ALADIN) on board the Atmospheric Dynamic Mission (ADM)-Aeolus satellite. Quality control procedures were developed to assess the HLOS wind data quality, and observation operators added to the KIM three-dimensional variational data assimilation system to support the new observed variables. In a global cycling experiment, assimilation of ALADIN observations led to reductions in average root-mean-square error of 2.1% and 1.3% for the zonal and meridional wind analyses when compared against European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) analyses. Even though the observable variable is wind, the assimilation of ALADIN observation had an overall positive impact on the analyses of other variables, such as temperature and specific humidity. As a result, the KIM 72-hour wind forecast fields were improved in the Southern Hemisphere poleward of 30 degrees.

• 한국재료학회지
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• 제33권5호
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• pp.175-188
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• 2023

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.

• 천문학회보
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• 제46권1호
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• pp.41.2-41.2
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• 2021

In this study, we generate He I 1083 nm images from Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images using a novel deep learning method (pix2pixHD) based on conditional Generative Adversarial Networks (cGAN). He I 1083 nm images from National Solar Observatory (NSO)/Synoptic Optical Long-term Investigations of the Sun (SOLIS) are used as target data. We make three models: single input SDO/AIA 19.3 nm image for Model I, single input 30.4 nm image for Model II, and double input (19.3 and 30.4 nm) images for Model III. We use data from 2010 October to 2015 July except for June and December for training and the remaining one for test. Major results of our study are as follows. First, the models successfully generate He I 1083 nm images with high correlations. Second, the model with two input images shows better results than those with one input image in terms of metrics such as correlation coefficient (CC) and root mean squared error (RMSE). CC and RMSE between real and AI-generated ones for the model III with 4 by 4 binnings are 0.84 and 11.80, respectively. Third, AI-generated images show well observational features such as active regions, filaments, and coronal holes. This work is meaningful in that our model can produce He I 1083 nm images with higher cadence without data gaps, which would be useful for studying the time evolution of chromosphere and coronal holes.

• Journal of Korea Trade
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• 제27권1호
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• pp.79-100
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• 2023

Purpose - The purpose of this study is to analyze the market power of the Korea Container Shipping Market (Intra Asia, Korea-Europe, and Korea-U.S.) to verify the existence of collusion empirically, and to answer whether the joint actions of liner market participants in Korea have formed market dominance for each route. Precisely, it will be verified through the Lerner index as to whether the regional market of Asia is a monopoly, oligopoly, or perfect competition. Design/methodology - This study used a Lerner index adjusted with elasticity presented in the New Imperial Organization (NEIO) studies. NEIO refers to a series of empirical studies that estimate parameters to judge market power from industrial data. This study uses B-L empirical models by Bresnahan (1982) and Lau (1982). In addition, NEIO research data statistically contain self-regression and stability problems as price and time series data. A dynamic model following Steen and Salvanes' Error Correction Model was used to solve this problem. Findings - The empirical results are as follows. First, λ, representing market power, is nearly zero in all three markets. Second, the Korean shipping market shows low demand elasticity on average. Nevertheless, the markup is low, a characteristic that is difficult to see in other industries. Third, the Korean shipping market generally remains close to perfect competition from 2014 to 2022, but extreme market power appears in a specific period, such as COVID-19. Fourth, there was no market power in the Intra Asia market from 2008 to 2014. Originality/value - Doubts about perfect competition in the liner market continued, but there were few empirical cases. This paper confirmed that the Korea liner market is a perfect competition market. This paper is the first to implement dynamics using ECM and recursive regression to demonstrate market power in the Korean liner market by dividing the shipping market into Deep Sea and Intra Asia separately. It is also the first to prove the most controversial problems in the current shipping industry numerically and academically.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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• pp.188-197
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• 2020

Due to hazardous working environments at complex, unstructured, and dynamic construction sites, workers frequently face potential safety and health risks throughout the construction process. In this regard, addressing safety challenges remains one of the top priorities. Construction workers' ability to identify and assess risks is acquired through training, which is one of the primary key factors to determine their safety and wellbeing in hazardous working environments. As such, safety managers constantly focus on the effectiveness of the training materials provided to the workers. However, the construction workers are considerably at greater risk of injuries and fatalities compared to the workers in other industries. In this regard, further studies are required to build up a body of knowledge on the conventional safety training approaches as well as their evaluation techniques in order to boost up the adoption by the practitioners in a widespread manner. This paper provides a systematic review of the current safety training approaches and the various techniques for measuring their effectiveness. The attributes of the current safety training methods for construction workers and their evaluation techniques are identified and analyzed. Results indicated that: 1) immersive environment-based training methods are effective than the traditional safety training methods; 2) this effectiveness can be empirically supported by evaluation strategies, but the current techniques are subjective, intrusive, and error-prone. This research offers fresh opportunities to investigate the training strategies by objectively monitoring the physiological responses of construction crews. The results of this study can be used by researchers and practitioners to identify and determine optimal safety training programs that could potentially become ubiquitous in the construction industry.

• 대기
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• 제33권5호
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• pp.549-560
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• 2023

This study evaluates three distinct observation methods, CRDS, OA-ICOS, and OF-CEAS, in greenhouse gas monitoring equipment for atmospheric CO₂ and CH₄ concentrations. The assessment encompasses fundamental performance, high-concentration measurement accuracy, calibration methods, and the impact of atmospheric humidity on measurement accuracy. Results indicate that within a range of approximately 500 ppm, all three devices demonstrate high accuracy and linearity. However, beyond 1000 ppm, CO₂ accuracy sharply declines (84%), emphasizing the need for caution when interpreting high-concentration CO₂ data. An analysis of calibration methods reveals that both CO₂ and CH₄ measurements achieve high accuracy and linearity through 1-point calibration, suggesting that multi-point calibration is not imperative for precision. In dynamic atmospheric conditions with significant CO₂ and CH₄ concentration variations, a 1-point calibration suffices for reliable data (99% accuracy). The evaluation of humidity impact demonstrates that humidity removal devices significantly reduce air moisture levels, yet this has a negligible effect on dry CO₂ concentrations (less than 0.5% relative error). All three observation method instruments, which have integrated humidity correction to calculate dry CO₂ concentrations, exhibit minor sensitivity to humidity removal devices, implying that additional removal devices may not be essential. Consequently, this study offers valuable insights for comparing data from different measurement devices and provides crucial information to consider in the operation of monitoring sites.

• 농업과학연구
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• 제50권4호
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• pp.773-784
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• 2023

In this study, we developed a dynamic model and steering controller model for an autonomous tractor and evaluated their performance. The traction force was measured using a 6-component load cell, and the rotational speed of the wheels was monitored using proximity sensors installed on the axles. Torque sensors were employed to measure the axle torque. The PI (proportional integral) controller's coefficients were determined using the trial-error method. The coefficient of the P varied in the range of 0.1 - 0.5 and the I coefficient was determined in 3 increments of 0.01, 0.05, and 0.1. To validate the simulation model, we conducted RMS (root mean square) comparisons between the measured data of axle torque and the simulation results. The performance of the steering controller model was evaluated by analyzing the damping ratio calculated with the first and second overshoots. The average front and rear axle torque ranged from 3.29 - 3.44 and 6.98 - 7.41 kNm, respectively. The average rotational speed of the wheel ranged from 29.21 - 30.55 rpm at the front, and from 21.46 - 21.63 rpm at the rear. The steering controller model exhibited the most stable control performance when the coefficients of P and I were set at 0.5 and 0.01, respectively. The RMS analysis of the axle torque results indicated that the left and right wheel errors were approximately 1.52% and 2.61% (at front) and 7.45% and 7.28% (at rear), respectively.

• 한국전산구조공학회논문집
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• 제37권3호
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• pp.179-186
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• 2024

본 논문에서는 충격파 형태의 폭발 하중을 받는 부재의 소성 범위를 고려한 SDOF 해석의 수정계수를 개발하였다. SDOF 해석의 수정계수는 MDOF 해석 결과 값을 비교하여 도출하였다. SDOF 해석에 영향을 미치는 매개변수로 부재의 경계조건, 폭발 하중 지속시간과 고유주기 비를 선정하였다. 수정계수는 탄성 하중-질량 변환 계수를 기준으로 산정하였다. 수정계수 곡선은 상한, 하한 매개변수 경계 사이에 있도록 타원 방정식을 이용하여 도출하였다. 서로 다른 단면과 경계조건을 가지는 예제에 수정계수를 적용한 결과 SDOF 해석의 오차율이 15%에서 3%로 감소하였다. 본 연구의 결과는 수정계수를 적용하여 SDOF 해석의 정확도를 높임에 따라 폭발 해석에 널리 활용될 수 있다.

• 한국인터넷방송통신학회논문지
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• 제24권1호
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• pp.197-205
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• 2024

자율주행 자동차 개발 및 상용화에 있어서 주행안전도 확보가 가장 중요한 시점에서 이를 위해 전방 및 주행차량 주변에 존재하는 다양한 정적/동적 차량의 인식과 검출 성능을 고도화 및 최적화하기 위한 AI, 빅데이터 기반 알고리즘개발 등이 연구되고 있다. 하지만 레이더와 카메라의 고유한 장점을 활용하여 동일한 차량으로 인식하기 위한 연구 사례들이 많이 있지만, 딥러닝 영상 처리 기술을 이용하지 않거나, 레이더의 성능상의 문제로 짧은 거리만 동일한 표적으로 감지하고 있다. 따라서 레이더 장비와 카메라 장비에서 수집할 수 있는 데이터셋을 구성하고, 데이터셋의 오차를 계산하여 동일한 표적으로 인식하는 융합 기반 차량 인식 방법이 필요하다. 본 논문에서는 레이더와 CCTV(영상) 설치 위치에 따라 동일한 객체로 판단하기에 데이터 오차가 발생하기 때문에 설치한 위치에 따라 위치 정보를 연동할 수 있는 기술 개발을 목표로 한다.