• Clinical and Experimental Pediatrics
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• v.65 no.5
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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.

• Transactions of the Korean hydrogen and new energy society
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• v.29 no.4
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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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2406-2414
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• 2013

The data-driven IT project model has been improved by adding processes such as analysis/design of data structure and channels of data collection/distribution, application of data standard and securing the flexibility in IT convergence devices data configuration on existing informatization project procedure driven by HW and SW function. This model focused on the evaluation of improvement effect which warrants data flexibility of IT convergence device. IT convergence device was divided into sensor and reactor and a situation when new information system is additionally linked to these devices was assumed. The system improvement complexity and index on network environment were estimated and they were compared to existing method.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.503-514
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• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.

• Journal of the Korean Society for Railway
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• v.10 no.2 s.39
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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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• v.32 no.3
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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.

• Journal of KIBIM
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• v.9 no.4
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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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• v.83 no.4
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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.

• Journal of the Korean Data and Information Science Society
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• v.21 no.2
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• pp.251-262
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• 2010

While outsourcing has become a basic strategy of the information system adoption, there is an emerging needs to analyze the gap between the required data and the existing data for the new system from an adopting company's perspective. In CRM adoption failure cases, the first reason is adopting company pay no attention to the data that will support investment and systems. So far, there is no attempt to consider data driven approach in information system adoption field. Hence, we propose Information System Adoption Model based on Data (ISAMD) and show how to use in real world by simulation. By using ISAMD, information system adoption decision maker can simulate the needed data and related cost with various information system alternatives in short term, and long term planning. ISAMD can prevent the possible threat of unexpected data cost in adopting new system at the adopting decision stage.

• Current Photovoltaic Research
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• v.10 no.1
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• pp.6-15
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• 2022

Climate change is among the most important issues facing mankind in modern society. However, global PV energy expansion has been driven mainly by OECD countries. We investigate the determinants of PV energy growth by panel data of selected OECD countries from 1991 to 2018. We investigate four categories of driving factors: socioeconomic, technological, country specific, and policy factors. The test results support that PV capacity growth is significantly driven by technology development and multidimensional environment policy factors. Socioeconomic factors such as CO₂, GDP, and electricity price are statistically significant on the growth of PV energy, too. Whereas, country-specific solar potential factor is the least related. As most of the socioeconomic factors are exogenous, we need to focus more on PV technology development and policy measures.