• Smart Structures and Systems
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• 제10권4_5호
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• pp.331-351
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• 2012

In this study an output-only system identification technique for civil structures under ambient vibrations is carried out, mainly focused on using the Stochastic Subspace Identification (SSI) based algorithms. A newly developed signal processing technique, called Singular Spectrum Analysis (SSA), capable to smooth a noisy signal, is adopted for preprocessing the measurement data. An SSA-based SSI algorithm with the aim of finding accurate and true modal parameters is developed through stabilization diagram which is constructed by plotting the identified system poles with increasing the size of data matrix. First, comparative study between different approaches, with and without using SSA to pre-process the data, on determining the model order and selecting the true system poles is examined in this study through numerical simulation. Finally, application of the proposed system identification task to the real large scale structure: Canton Tower, a benchmark problem for structural health monitoring of high-rise slender structures, using SSA-based SSI algorithm is carried out to extract the dynamic characteristics of the tower from output-only measurements.

• Clinical and Experimental Pediatrics
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• 제65권5호
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• pp.239-249
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• 2022

Cells survive and proliferate through complex interactions among diverse molecules across multiomics layers. Conventional experimental approaches for identifying these interactions have built a firm foundation for molecular biology, but their scalability is gradually becoming inadequate compared to the rapid accumulation of multiomics data measured by high-throughput technologies. Therefore, the need for data-driven computational modeling of interactions within cells has been highlighted in recent years. The complexity of multiomics interactions is primarily due to their nonlinearity. That is, their accurate modeling requires intricate conditional dependencies, synergies, or antagonisms between considered genes or proteins, which retard experimental validations. Artificial intelligence (AI) technologies, including deep learning models, are optimal choices for handling complex nonlinear relationships between features that are scalable and produce large amounts of data. Thus, they have great potential for modeling multiomics interactions. Although there exist many AI-driven models for computational biology applications, relatively few explicitly incorporate the prior knowledge within model architectures or training procedures. Such guidance of models by domain knowledge will greatly reduce the amount of data needed to train models and constrain their vast expressive powers to focus on the biologically relevant space. Therefore, it can enhance a model's interpretability, reduce spurious interactions, and prove its validity and utility. Thus, to facilitate further development of knowledge-guided AI technologies for the modeling of multiomics interactions, here we review representative bioinformatics applications of deep learning models for multiomics interactions developed to date by categorizing them by guidance mode.

• 한국수소및신에너지학회논문집
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• 제29권4호
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• pp.339-356
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• 2018

Although fuel cell systems have advantages in terms of electric efficiency and environmental impact compared with conventional power systems, fuel cell systems have not been deployed widely due to their low reliability and high price. In order to guarantee the lifetime of 10 years, which is the commercialization goal of Polymer electrolyte fuel cells (PEFCs), it is necessary to improve durability and reliability through optimized operation and maintenance technologies. Due to the complexity of components and their degradation phenomena, it's not easy to develop and apply the diagnose and prognostic methodologies for PEFCs. The purpose of the paper is to show the current state on PEFC prognostic technology for condition based maintenance. For the prognostic of PEFCs, the model driven method, the data-driven, and the hybrid method can be applied. The methods reviewed in this paper can contribute to the development of technologies to reduce the life cycle cost of fuel cells and increase the reliability through prognostics-based health management system.

• 농업과학연구
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• 제51권3호
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• pp.315-329
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• 2024

This study was conducted for simulation model development of an electric all-wheel-drive vehicle to adapt the agricultural machinery. Data measurement system was installed on a four-wheel electric driven vehicle using proximity sensor, torque-meter, global positioning system (GPS) and data acquisition (DAQ) device. Axle torque and rotational speed were measured using a torque-meter and a proximity sensor. Driving test was performed on an upland field at a speed of 7 km·h^-1. Simulation model was developed using a multi-body dynamics software, and tire properties were measured and calculated to reflect the similar road conditions. Measured and simulated data were compared to validate the developed simulation model performance, and axle rotational speed was selected as simulation input data and axle torque and power were selected as simulation output data. As a result of driving performance, an average axle rotational speed was 115 rpm for each wheel. Average axle torque and power were 4.50, 4.21, 4.04, and 3.22 Nm and 53.42, 50.56, 47.34, and 38.07 W on front left, front right, rear left, and rear right wheel, respectively. As a result of simulation driving, average axle torque and power were 4.51, 3.9, 4.16, and 3.32 Nm and 55.79, 48.11, 51.62, and 41.2 W on front left, front right, rear left, and rear right wheel, respectively. Absolute error of axle torque was calculated as 0.22, 7.36, 2.97, and 3.11% on front left, front right, rear left, rear right wheel, respectively, and absolute error of axle power was calculated as 4.44, 4.85, 9.04, and 8.22% on front left, front right, rear left, and rear right wheel, respectively. As a result of absolute error, it was shown that developed simulation model can be used for driving performance prediction of electric driven vehicle. Only straight driving was considered in this study, and various road and driving conditions would be considered in future study.

• 한국수자원학회논문집
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• 제51권6호
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• pp.503-514
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• 2018

최근 기후변화 및 유역개발로 인하여 메콩강 유역의 수문환경이 급격히 변화하고 있으며, 메콩강을 공유하는 국가의 수재해 예방 및 지속가능한 수자원개발을 위해서는 메콩강 주요지점에서의 유량 정보의 분석 및 예측이 요구된다. 본 연구에서는 물리적 기반의 수문모형인 SWAT과 데이터기반 딥러닝 알고리즘인 LSTM을 이용하여 메콩강 하류 Kratie 지점의 유출모의를 수행하고, 유출모의 정확도 및 두 가지 방법론의 장 단점을 비교 분석한다. SWAT 모형의 구축을 위해 범용 입력자료(지형: HydroSHED, 토지이용: GLCF-MODIS, 토양: FAO-Soil map, 강우: APHRODITE 등)을 이용하였으며 warming-up 및 매개변수 보정 후 2003~2007년 일유량 모의를 수행하였다. LSTM을 이용한 유출모의의 경우, 딥러닝 오픈소스 라이브러리인 TensorFlow를 활용하여 Kratie 지점기준 메콩강 상류 10개 수위관측소의 두 기간(2000~2002, 2008~2014) 일수위 정보만을 이용하여 심층신경망을 학습하고, SWAT 모형과 마찬가지로 2003~2007년을 대상으로 Kratie 지점에 대한 일수위 모의 후 수위-유량관계곡선식을 이용하여 유출량으로 환산하였다. 두 모형의 모의성능 비교 검토를 위하여 모의기간에 대해 NSE (Nash-Sutcliffe Efficiency)을 산정한 결과, SWAT은 0.9, LSTM은 보다 높은 0.99의 정확도를 나타내는 것으로 분석되었다. 메콩강과 같은 대유역의 특정 지점에 대한 수문시계열 자료의 모의를 위해서는 다양한 입력자료를 요구하는 물리적 수문모형 대신 선행 시계열자료의 변동성을 기억 학습하여 이를 예측에 반영하는 LSTM 기법 등 데이터기반의 심층신경망 모형의 적용이 가능할 것으로 판단된다.

• 한국산학기술학회논문지
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• 제14권5호
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• pp.2406-2414
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• 2013

데이터 중심 정보시스템 구축 모델은 그간 정보화 사업 추진이 HW, SW기능 중심으로 이루어지던 것을, 데이터 구조 및 데이터 수집 배포 채널에 대한 체계적인 분석과 설계, 데이터표준 적용, 정보통신 융합기기의 데이터 설정의 유연성 확보 등의 고려하여 개선한 모델이다. 이 모델의 주요 특성인 정보통신 융합기기에 데이터 유연성이 보장되는 개선 효과에 대해 센서와 반응기로 구분하여 이들 기기에 새로운 정보시스템이 추가적으로 연계되는 상황을 가정하여 시스템 개선 복잡도와 네트워크 환경에 대한 지수를 산정하여 기존의 일반적인 구축 방식과 비교하였다. 본 모델을 확산함으로써, 일반 업무용 정보시스템 외에 나날이 늘어나는 정보기술 융 복합 기기들이 처리하는 데이터에 대해서도 품질과 상호운용성을 통제하게 되어, 정보화 거버넌스의 영역 확대를 통해 종합적인 정보화 기획 및 성과 관리 등이 가능하게 되는 개선 효과가 있다.

• 한국철도학회논문집
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• 제10권2호
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• pp.224-230
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• 2007

The performance and applicability of FDS code is analyzed for flow simulation in railway tunnel. FDS has been built in NIST(USA) for simulation of fire-driven flow. RANS and DNS's results are compared with FDS's. AJL non-linear ${\kappa}-{\epsilon}$[7,8] model is employed to calculate the turbulent flow for RANS. DNS data by Moser et al.[9] are used to prove the FDS's applicability in the near wall region. Parallel plate is used for simplified model of railway tunnel. Geometrical variables are non-dimensionalized by the height (H) of parallel plate. The length of streamwise direction is 50H and the length of spanwise direction is 5H. Selected Re numbers are 10,667 for turbulent flow and 133 for laminar low. The characteristics of turbulent boundary layer are introduced. AJL model's predictions of turbulent boundary layer are well agreed with DNS data. However, the near wall turbulent boundary layer is not well resolved by FDS code. Slip conditions are imposed on the wall but wall functions based on log-law are not employed by FDS. The heavily dense grid distribution in the near wall region is necessary to get correct flow behavior in this region for FDS.

• Journal of Forest and Environmental Science
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• 제32권3호
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• pp.253-261
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• 2016

Xanthoceras sorbifolia seed coat (XSSC) is a processing residue of the bioenergy crop. This work aimed to evaluate the applicability of using the steam explosion to modify the residue for dye biosorption from aqueous solutions by using methylene blue as a model cationic dye. Equilibrium, kinetic and thermodynamic parameters for the biosorption of methylene blue on the steam-exploded XSSC (SE-XSSC) were evaluated. The kinetic data followed the pseudo-second-order model, and the rate-limiting step was the chemical adsorption. Intraparticle diffusion was one of the rate-controlling factors. The equilibrium data agreed well with the Langmuir isotherm, and the biosorption was favorable. The steam-explosion pretreatment strongly affected the biosorption in some respects. It reduced the adsorption rate constant and the initial sorption rate of the pseudo-second-order model. It enhanced the adsorption capacity of methylene blue at higher temperatures while reduced the capacity at lower ones. It changed the biosorption from an exothermic process driven by both the enthalpy and the entropy to an endothermic one driven by entropy only. It increased the surface area and decreased the pH point of zero charge of the biomass. Compared with the native XSSC, SE-XSSC is preferable to MB biosorption from warmer dye effluents.

• 한국BIM학회 논문집
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• 제9권4호
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• pp.41-50
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• 2019

Digital models for a cable-stayed bridge are defined considering data-driven engineering from design to construction. Algorithms for digital object generation of each component of the cable-stayed bridge were developed. Using these algorithms, Level-3 BIM practices can be realized from design stages. Based on previous practices, digital object library can be accumulated. Basic digital models are modified according to given design conditions by a designer. Once design models are planned, various applications using the models are linked the models such as estimation, drawings and mechanical properties. Federated bridge models are delivered to construction stages. In construction stage, the models can be efficiently revised according to the changed situations during construction phases. In this paper, measured coordinates are imported to the model generation algorithms and revised models are obtained. Augmented reality devices and their applications are proposed. AR simulations in construction site and in office condition are tested. From this pilot test of digital models, it can be said that Level-3 BIM practices can be realized by using in-house modeling algorithms according to different purposes.

• Structural Engineering and Mechanics
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• 제83권4호
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• pp.537-549
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• 2022

In this paper, a novel multi-feature model predictive control (MPC) framework with real-time and adaptive performances is proposed for intelligent structural control in which some drawbacks of the algorithm including, complex control rule and non-optimality, are alleviated. Hence, Linear Programming (LP) is utilized to simplify the resulted control rule. Afterward, the Whale Optimization Algorithm (WOA) is applied to the optimal and adaptive tuning of the LP weights independently at each time step. The stochastic control rule is also achieved using Kalman Filter (KF) to handle noisy measurements. The Extreme Learning Machine (ELM) is then adopted to develop a data-driven and real-time control algorithm. The efficiency of the developed algorithm is then demonstrated by numerical simulation of a twenty-story high-rise benchmark building subjected to earthquake excitations. The competency of the proposed method is proven from the aspects of optimality, stochasticity, and adaptivity compared to the KF-based MPC (KMPC) and constrained MPC (CMPC) algorithms in vibration suppression of building structures. The average value for performance indices in the near-field and far-field (El earthquakes demonstrates a reduction up to 38.3% and 32.5% compared with KMPC and CMPC, respectively.