• Smart Structures and Systems
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• v.30 no.2
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• pp.145-157
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• 2022

The design of safe and economical structures depends on the reliable live load from load survey. Live load surveys are traditionally conducted by randomly selecting rooms and weighing each item on-site, a method that has problems of low efficiency, high cost, and long cycle time. This paper proposes a deep learning-based method combined with Internet big data to perform live load surveys. The proposed survey method utilizes multi-source heterogeneous data, such as images, voice, and product identification, to obtain the live load without weighing each item through object detection, web crawler, and speech recognition. The indoor objects and face detection models are first developed based on fine-tuning the YOLOv3 algorithm to detect target objects and obtain the number of people in a room, respectively. Each detection model is evaluated using the independent testing set. Then web crawler frameworks with keyword and image retrieval are established to extract the weight information of detected objects from Internet big data. The live load in a room is derived by combining the weight and number of items and people. To verify the feasibility of the proposed survey method, a live load survey is carried out for a meeting room. The results show that, compared with the traditional method of sampling and weighing, the proposed method could perform efficient and convenient live load surveys and represents a new load research paradigm.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.475-483
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• 2023

The high-flux advanced neutron application reactor (HANARO) is a multi-purpose research reactor at the Korea Atomic Energy Research Institute (KAERI). HANARO has been used in scientific and industrial research and developments. Therefore, stable operation is necessary for national science and industrial prospects. This study proposed an anomaly detection system based on deep learning, that supports the stable operation of HANARO. The proposed system collects multiple sensor data, displays system information, analyzes status, and performs anomaly detection using deep autoencoder. The system comprises communication, visualization, and anomaly-detection modules, and the prototype system is implemented on site in 2021. Finally, an analysis of the historical data and synthetic anomalies was conducted to verify the overall system; simulation results based on the historical data show that 12 cases out of 19 abnormal events can be detected in advance or on time by the deep learning AD model.

• The Journal of the Acoustical Society of Korea
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• v.34 no.6
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• pp.423-431
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• 2015

KIOST-HYU joint acoustics experiment was performed on the western shallow water off the Taean peninsula in the Yellow Sea in May 2013. In this paper, mid-frequency (6~16 kHz) bottom loss data measured in a grazing angle range of $17{\sim}60^{\circ}$ are presented and compared to the predictions obtained using a Rayleigh reflection model. The sediment structure of the experimental site was characterized by multi-layered sediment and the components of the surficial sediment consisted of various types of particles with a mean grain size of $5.9{\phi}$. The model predictions obtained using the mean grain size were not in agreement with the measured bottom loss, and those obtained using the grain size of $4{\phi}$, which was estimated by an inversion process, showed a best fit to the measurements. It would be because the standard deviation of the gain-size distribution of surficial sediment is $4.3{\phi}$, which is much larger than those of other areas around the experimental site. Finally, the model predictions obtained using the geoacoustic parameters estimated from the inversion process for the surficial sediment layer and those corresponding to the mean grain size of $1.3{\phi}$ for lower layer are reasonably agreement with the measured bottom loss data.

• Journal of Korea Water Resources Association
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• v.53 no.9
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• pp.647-660
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• 2020

The objectives of this study are to develop a framework for selecting multi-GCMs considering Asia monsoon characteristics and assess it's applicability. 12 climate variables related to monsoon climates are selected for GCM selection. The framework for selecting multi-GCMs includes the evaluation matrix of GCM performance based on their capability to simulate historical climate features. The climatological patterns of 12 variables derived from individual GCM over the summer monsoon season during the past period (1976-2005) and they are compared against observations to evaluate GCM performance. For objective evaluation, a rigorous scoring rule is implemented by comparing the GCM performance based on the results of statistics between historical simulation derived from individual GCM and observations. Finally, appropriate 5 GCMs (NorESM1-M, bcc-csm1-m, CNRM-CM5, CMCC-CMS, and CanESM2) are selected in consideration of the ranking of GCM and precipitation performance of each GCM. The selected 5 GCMs are compared with the historical observations in terms of monsoon season and monthly mean to validate their applicability. The 5 GCMs well capture the observational climate characteristics of Asia for the 12 climate variables also they reduce the bias between the entire GCM simulations and the observational data. This study demonstrates that it is necessary to consider various climate variables for GCM selection and, the method introduced in this study can be used to select more reliable climate change scenarios for climate change assessment in the Asia region.

• Journal of Wetlands Research
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• v.18 no.2
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• pp.166-172
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• 2016

Accurate understanding of land surface is essential to analyze energy exchanges between earth surface and atmosphere. For the quantization of energy fluxes, the various researches about Land Surface Model(LSM) have been progressed. Among the various LSMs, the researches using Common Land Model(CLM) and Variable Infiltration Capacity(VIC) model are performed briskly. The CLM which is advanced LSM can calculate realistic results with few user defined parameters. The VIC model which is also typical LSM is widely used for estimation of energy fluxes and runoff in various fields. In this study, the energy fluxes which are net radiation, sensible heat flux, and latent heat flux were estimated using CLM and VIC model at Southern Sierra-Critical Zone Observatory(SS-CZO) site in California, United States. In case of net radiation and sensible heat flux, both models showed good agreement with observations, however, the CLM showed underestimated patterns of net radiation and sensible heat flux during precipitation period. In case of latent heat flux, the CLM represented better estimation of latent heat flux than VIC model which underestimated the latent heat flux. Through the estimation of energy fluxes and analysis of models' pros and cons, the applicability of CLM and VIC models and need of multi-model application were identified.

• Ocean Science Journal
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• v.43 no.4
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• pp.195-208
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• 2008

This paper evaluates whether a thermodynamic ocean-carbon model can be used to predict the monthly mean global fields of the surface-water partial pressure of $CO_2$ ($pCO_{2SEA}$) from sea surface salinity (SSS), temperature (SST), and/or nitrate ($NO_3$) concentration using previously published regional total inorganic carbon ($C_T$) and total alkalinity ($A_T$) algorithms. The obtained $pCO_{2SEA}$ values and their amplitudes of seasonal variability are in good agreement with multi-year observations undertaken at the sites of the Bermuda Atlantic Timeseries Study (BATS) ($31^{\circ}50'N$, $60^{\circ}10'W$) and the Hawaiian Ocean Time-series (HOT) ($22^{\circ}45'N$, $158^{\circ}00'W$). By contrast, the empirical models predicted $C_T$ less accurately at the Kyodo western North Pacific Ocean Time-series (KNOT) site ($44^{\circ}N$, $155^{\circ}E$) than at the BATS and HOT sites, resulting in greater uncertainties in $pCO_{2SEA}$ predictions. Our analysis indicates that the previously published empirical $C_T$ and $A_T$ models provide reasonable predictions of seasonal variations in surface-water $pCO_{2SEA}$ within the (sub) tropical oceans based on changes in SSS and SST; however, in high-latitude oceans where ocean biology affects $C_T$ to a significant degree, improved $C_T$ algorithms are required to capture the full biological effect on $C_T$ with greater accuracy and in turn improve the accuracy of predictions of $pCO_{2SEA}$.

• Journal of the Korean association of regional geographers
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• v.18 no.4
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• pp.414-432
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• 2012

Land price is a kind of text to read urban spatial structure. The purpose of this paper is to inquire into the characteristics of Daegu's urban structure and its change in time through exploring spatio-temporal variations of land price with a detailed spatial and temporal resolution. To achieve this, land value surfaces were represented using the officially assessed land price every other year from 1995 to 2011. Through mapping and exploring spatio-temporal patterns and fluctuation rates of land price for this period, changes in urban structure, the effects of local decision makings such as Greenbelt adjustment, housing site development, and gentrification, and the effects of business fluctuations or policies at global or national scales could be caught. In addition, the trends for suburbanization and multi-centric urban form could be examined from the results of a negative exponential model explaining the effect of distance from an urban center on spatial variation of land price. These results demonstrate that urban analysis using land price mirroring spatial decision making at various scales could deepen understanding for internal structure and change of a city and provide useful information for establishing regional and urban development policies and evaluating their effects.

• Computers and Concrete
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• v.21 no.5
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• pp.495-504
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• 2018

The possibility of earthquakes in vulnerable regions indicates that efficient technique is required for seismic protection of buildings. During the recent decades, the concept is moving towards the insertion of base isolation on seismic prone buildings. So, investigation of structural behavior is a burning topic for buildings to be isolated in base level by bearing device. This study deals with the incorporation of base isolation system and focuses the changes of structural responses for different types of Lead Rubber Bearing (LRB) isolators. A number of sixteen model buildings have been simulated selecting twelve types of bearing systems as well as conventional fixed-base (FB) scheme. The superstructures of the high-rise buildings are represented by finite element assemblage adopting multi-degree of freedoms. Static and dynamic analyses are carried out for FB and base isolated (BI) buildings. The dynamic analysis in finite element package has been performed by the nonlinear time history analysis (THA) based on the site-specific seismic excitation and compared employing eminent earthquakes. The influence of the model type and the alteration in superstructure behavior of the isolated buildings have been duly assessed. The results of the 3D multistory structures show that the lateral forces, displacement, inertia and story accelerations of the superstructure of the seismic prone buildings are significantly reduced due to bearing insertion. The nonlinear dynamic analysis shows 12 to 40% lessening in base shear when LRB is incorporated leading to substantial allowance of horizontal displacement. It is revealed that the LRB isolators might be potential options to diminish the respective floor accelerations, inertia, displacements and base shear whatever the condition coincides. The isolators with lower force intercept but higher isolation period is found to be better for decreasing base shear, floor acceleration and inertia force leading to reduction of structural and non-structural damage. However, LRB with lower isolator period seems to be more effective in dropping displacement at bearing interface aimed at reducing horizontal shift of building structure.

• The Journal of the Korea Contents Association
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• v.16 no.12
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• pp.373-382
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• 2016

Carbon dioxide Capture and Storage/Sequestration (CCS) technology has attracted attention as an ideal method for most carbon dioxide reduction needs. When the collected carbon dioxide is transported to storage via pipelines, the direct transport is made if the storage is close, otherwise it can also be transported via an intermediate storage hub. Determining the number and the location of the intermediate storage hubs is an important problem. A decision-making algorithm using a mathematical model for solving the problem requires considerably more variables and constraints to describe the multi-objective decision, but the computational complexity of the problem increases and it also does not guarantee the optimality. This research proposes an algorithm to determine the location and the number of the intermediate storage hub and develop a simulator for the connection network of the carbon dioxide emission site. The simulator also provides the course of transportation of the carbon dioxide. As a case study, this model is applied to Korea.

• The KIPS Transactions:PartD
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• v.16D no.3
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• pp.433-440
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• 2009

As information technology of constructional area develops recently, web-based on-line system is rapidly increasing to provide information on diverse constructional materials so as to enhance productivity of constructional business and to reduce cost. Since the constructional materials information provided by these systems, i.e., quality, specification, etc are not standardized, however, the staffs on the constructional site suffer considerable difficulties in using materials information when acquiring information on specific materials, e.g., using diverse information systems or repeating similar jobs. Thus, this research typified information items of constructional materials on the basis of GDAS and designed multi system model to control integrated information on constructional materials. This system can efficiently control and utilize materials information by supporting automatic classification of constructional materials to which OmniClass Part-22 and UNSPSC are applied, conditional complex retrieval of materials information, real-time automatic embodiment of electronic catalog and retrieving/controlling RFID.