• Steel and Composite Structures
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• 제43권5호
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• pp.625-637
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• 2022

Axial compression capacity (P_u) is a significant yet complex parameter of concrete-filled steel tube (CFST) columns. This study offers a novel ensemble tool, adaptive neuro-fuzzy inference system (ANFIS) supervised by equilibrium optimization (EO), for accurately predicting this parameter. Moreover, grey wolf optimization (GWO) and Harris hawk optimizer (HHO) are considered as comparative supervisors. The used data is taken from earlier literature provided by finite element analysis. ANFIS is trained by several population sizes of the EO, GWO, and HHO to detect the best configurations. At a glance, the results showed the competency of such ensembles for learning and reproducing the P_u behavior. In details, respective mean absolute errors along with correlation values of 4.1809% and 0.99564, 10.5947% and 0.98006, and 4.8947% and 0.99462 obtained for the EO-ANFIS, GWO-ANFIS, and HHO-ANFIS, respectively, indicated that the proposed EO-ANFIS can analyze and predict the behavior of CFST columns with the highest accuracy. Considering both time and accuracy, the EO provides the most efficient optimization of ANFIS and can be a nice substitute for experimental approaches.

• Smart Structures and Systems
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• 제30권2호
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• pp.195-207
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• 2022

Computational drawbacks associated with regular predictive models have motivated engineers to use hybrid techniques in dealing with complex engineering tasks like simulating the compressive strength of concrete (CSC). This study evaluates the efficiency of tree potential metaheuristic schemes, namely shuffled complex evolution (SCE), multi-verse optimizer (MVO), and beetle antennae search (BAS) for optimizing the performance of a multi-layer perceptron (MLP) system. The models are fed by the information of 1030 concrete specimens (where the amount of cement, blast furnace slag (BFS), fly ash (FA1), water, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA2) are taken as independent factors). The results of the ensembles are compared to unreinforced MLP to examine improvements resulted from the incorporation of the SCE, MVO, and BAS. It was shown that these algorithms can considerably enhance the training and prediction accuracy of the MLP. Overall, the proposed models are capable of presenting an early, inexpensive, and reliable prediction of the CSC. Due to the higher accuracy of the BAS-based model, a predictive formula is extracted from this algorithm.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.25-25
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• 2023

The ensemble optimal interpolation (EnOI) scheme is a sub-optimal alternative to the ensemble Kalman filter (EnKF) with a reduced computational demand making it potentially more suitable for operational applications. Since only one model is integrated forward instead of an ensemble of model realizations, online estimation of the background error covariance matrix is not possible in the EnOI scheme. In this study, we investigate two Gaussian noise based ensemble generation strategies to produce dynamic covariance matrices for assimilation of water level observations into a distributed hydrological model. In the first approach, spatially correlated noise, sampled from a normal distribution with a fixed fractional error parameter (which controls its standard deviation), is added to the model forecast state vector to prepare the ensembles. In the second method, we use an adaptive error estimation technique based on the innovation diagnostics to estimate this error parameter within the assimilation framework. The results from a real and a set of synthetic experiments indicate that the EnOI scheme can provide better results when an optimal EnKF is not identified, but performs worse than the ensemble filter when the true error characteristics are known. Furthermore, while the adaptive approach is able to reduce the sensitivity to the fractional error parameter affecting the first (non-adaptive) approach, results are usually worse at ungauged locations with the former.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.205-205
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• 2021

Surface soil moisture, which governs the partitioning of precipitation into infiltration and runoff, plays an important role in the hydrological cycle. The assimilation of satellite soil moisture retrievals into a land surface model or hydrological model has been shown to improve the predictive skill of hydrological variables. This study aims to improve streamflow prediction with Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro) by assimilating Soil Moisture Active and Passive (SMAP) data at 3 km and analyze its impacts on hydrological components. We applied Cumulative Distribution Function (CDF) technique to remove the bias of SMAP data and assimilate SMAP data (April to July 2015-2019) into WRF-Hydro by using an Ensemble Kalman Filter (EnKF) with a total 12 ensembles. Daily inflow and soil moisture estimates of major dams (Soyanggang, Chungju, Sumjin dam) of South Korea were evaluated. We investigated how hydrologic variables such as runoff, evaporation and soil moisture were better simulated with the data assimilation than without the data assimilation. The result shows that the correlation coefficient of topsoil moisture can be improved, however a change of dam inflow was not outstanding. It may attribute to the fact that soil moisture memory and the respective memory of runoff play on different time scales. These findings demonstrate that the assimilation of satellite soil moisture retrievals can improve the predictive skill of hydrological variables for a better understanding of the water cycle.

• Steel and Composite Structures
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• 제44권6호
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• pp.867-882
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• 2022

Predicting the compressive strength of concrete (CSoC) is of high significance in civil engineering. The CSoC is a highly dependent and non-linear parameter that requires powerful models for its simulation. In this work, two novel optimization techniques, namely evaporation rate-based water cycle algorithm (ER-WCA) and equilibrium optimizer (EO) are employed for optimally finding the parameters of a multi-layer perceptron (MLP) neural processor. The efficiency of these techniques is examined by comparing the results of the ensembles to a conventionally trained MLP. It was observed that the ER-WCA and EO optimizers can enhance the training accuracy of the MLP by 11.18 and 3.12% (in terms of reducing the root mean square error), respectively. Also, the correlation of the testing results climbed from 78.80% to 82.59 and 80.71%. From there, it can be deduced that both ER-WCA-MLP and EO-MLP can be promising alternatives to the traditional approaches. Moreover, although the ER-WCA enjoys a larger accuracy, the EO was more efficient in terms of complexity, and consequently, time-effectiveness.

• 한국산업융합학회 논문집
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• 제26권6_3호
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• pp.1205-1213
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• 2023

This study delves into methods for enhancing the power efficiency of air compressor systems, with the primary objective of significantly impacting industrial energy consumption and environmental preservation. The paper scrutinizes Shinhan Airro Co., Ltd.'s power efficiency optimization technology and employs machine learning ensemble models to simulate power efficiency optimization. The results indicate that Shinhan Airro's optimization system led to a notable 23.5% increase in power efficiency. Nonetheless, the study's simulations, utilizing machine learning ensemble techniques, reveal the potential for a further 51.3% increase in power efficiency. By continually exploring and advancing these methodologies, this research introduces a practical approach for identifying optimization points through data-driven simulations using machine learning ensembles.

• Bulletin of the Korean Chemical Society
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• 제33권3호
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• pp.933-940
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• 2012

Recently, it has been shown that quantum coherence appears in energy transfers of various photosynthetic lightharvesting complexes at from cryogenic to even room temperatures. Because the photosynthetic systems are inherently complex, these findings have subsequently interested many researchers in the field of both experiment and theory. From the theoretical part, simplified dynamics or semiclassical approaches have been widely used. In these approaches, the quantum-classical Liouville equation (QCLE) is the fundamental starting point. Toward the semiclassical scheme, approximations are needed to simplify the equations of motion of various degrees of freedom. Here, we have adopted the Poisson bracket mapping equation (PBME) as an approximate form of QCLE and applied it to find the time evolution of the excitation in a photosynthetic complex from marine algae. The benefit of using PBME is its similarity to conventional Hamiltonian dynamics. Through this, we confirmed the coherent population transfer behaviors in short time domain as previously reported with a more accurate but more time-consuming iterative linearized density matrix approach. However, we find that the site populations do not behave according to the Boltzmann law in the long time limit. We also test the effect of adding spurious high frequency vibrations to the spectral density of the bath, and find that their existence does not alter the dynamics to any significant extent as long as the associated reorganization energy is changed not too drastically. This suggests that adopting classical trajectory based ensembles in semiclassical simulations should not influence the coherence dynamics in any practical manner, even though the classical trajectories often yield spurious high frequency vibrational features in the spectral density.

• 한국인터넷방송통신학회논문지
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• 제12권2호
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• pp.43-52
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• 2012

본 논문에서는 단일 주파수 망(Single Frequency Network: SFN)으로 ATSC(Advanced Television Systems Committee) 지상파 디지털 TV 방송을 서비스하기 위한 간섭 제거 디지털 동일 재생중계기(Interference Cancellation Digital On Channel Regenerative Repeater: IC-DOCR)에 대한 실험실 테스트 결과를 기술하고, 그 결과를 분석한다. IC-DOCR 실험실 테스트는 수신부 테스트, 송신부 테스트, 그리고 궤환 제거 테스트로 구분된다. 수신부 테스트는 랜덤 잡음, 단일 에코, 다중경로 앙상블, 인접 채널 테스트 등을 포함하고, 송신부 테스트는 대역외 방사, 송신신호의 품질, 위상 잡음 테스트 등을 포함한다. 실험실 테스트 결과에 의하면, IC-DOCR은 수신신호 보다 28dB가 높은 궤환 신호를 제거할 수 있으며, 랜덤 잡음에 대한 TOV(Threshold Of Visibility)는 17.8dB이다. 또한 송신부 출력 신호는 미국의 FCC(Federal Communications Commission)규격을 만족하고, SNR 30dB 이상의 송신신호 품질을 유지한다.

• 복식문화연구
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• 제28권4호
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• pp.463-479
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• 2020

The purpose of this study identify the types and characteristics of Hanbok worn in lunar new year parade and Korean parade in New York over the past three years. As for the research method, I first collected domestic news articles, overseas Korean news articles, and literature reviews about the history of the Korean parade in New York and the events of each year. Second, I conducted a case study on Hanbok worn in the six parades from 2017 to 2019 using photo data collected through direct surveys, 'YouTube' video footage and news article photos. Third, I interviewed people related to the New York parade about the route of buying and renting the Hanbok in the New York area. The types of Hanbok shown in the Korean parade in New York can be largely classified as ceremonial, performance, and daily costume. There were 65 ceremonial costumes in all, with men wearing Dallyeong (official's robe) and Gorlyongpo (dragon robe) and women wearing Dangui (woman's semi-formal jacket), Wonsam (woman's ceremonial robe), and Hwarot (princess's ceremonial robe). A number of performance costumes were also worn by the Chwitasu bands, Nongak ensembles and fan dancers. Finally, of the 210 daily Hanbok, most men wore Baji (pants) and Jeogori (jacket), and women wore Chima (skirts) and Jeogori. The parade attempts to showcase the beauty of Hanbok, but the costumes repeat year after year, which can feel monotonous to onlookers. This research can be used as a reference to effectively utilize Hanbok in future Korean cultural events.

• Asian review of World Histories
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• 제4권1호
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• pp.3-34
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• 2016

This introduction is both a statement of a research problem and an account of the first research results for its solution. As more historical databases come online and overlap in coverage, we need to discuss the two main issues that prevent 'big' results from emerging so far. Firstly, historical data are seen by computer science people as unstructured, that is, historical records cannot be easily decomposed into unambiguous fields, like in population (birth and death records) and taxation data. Secondly, machine-learning tools developed for structured data cannot be applied as they are for historical research. We propose a complex network, narrative-driven approach to mining historical databases. In such a time-integrated network obtained by overlaying records from historical databases, the nodes are actors, while thelinks are actions. In the case study that we present (the world as seen from Venice, 1205-1533), the actors are governments, while the actions are limited to war, trade, and treaty to keep the case study tractable. We then identify key periods, key events, and hence key actors, key locations through a time-resolved examination of the actions. This tool allows historians to deal with historical data issues (e.g., source provenance identification, event validation, trade-conflict-diplomacy relationships, etc.). On a higher level, this automatic extraction of key narratives from a historical database allows historians to formulate hypotheses on the courses of history, and also allow them to test these hypotheses in other actions or in additional data sets. Our vision is that this narrative-driven analysis of historical data can lead to the development of multiple scale agent-based models, which can be simulated on a computer to generate ensembles of counterfactual histories that would deepen our understanding of how our actual history developed the way it did. The generation of such narratives, automatically and in a scalable way, will revolutionize the practice of history as a discipline, because historical knowledge, that is the treasure of human experiences (i.e. the heritage of the world), will become what might be inherited by machine learning algorithms and used in smart cities to highlight and explain present ties and illustrate potential future scenarios and visionarios.