• Korean Journal of Environmental Biology
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• v.41 no.4
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• pp.505-518
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• 2023

The key to invasive pest management lies in preemptive action. However, most current research using species distribution models is conducted after an invasion has occurred. This study modeled the potential distribution of the globally notorious sweet potato pest, the sweet potato weevil(Cylas formicarius), that has not yet invaded Korea using MaxEnt. Using global occurrence data, bioclimatic variables, and topsoil characteristics, MaxEnt showed high explanatory power as both the training and test areas under the curve exceeded 0.9. Among the environmental variables used in this study, minimum temperature in the coldest month (BIO06), precipitation in the driest month (BIO14), mean diurnal range (BIO02), and bulk density (BDOD) were identified as key variables. The predicted global distribution showed high values in most countries where the species is currently present, with a significant potential invasion risk in most South American countries where C. formicarius is not yet present. In Korea, Jeju Island and the southwestern coasts of Jeollanam-do showed very high probabilities. The impact of climate change under shared socioeconomic pathway (SSP) scenarios indicated an expansion along coasts as climate change progresses. By applying the 10th percentile minimum training presence rule, the potential area of occurrence was estimated at 1,439 km² under current climate conditions and could expand up to 9,485 km² under the SSP585 scenario. However, the model predicted that an inland invasion would not be serious. The results of this study suggest a need to focus on the risk of invasion in islands and coastal areas.

• Journal of Environmental Impact Assessment
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• v.30 no.5
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• pp.271-296
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• 2021

Since the thermal stratification in a reservoir inhibits the vertical mixing of the upper and lower layers and causes the formation of a hypoxia layer and the enhancement of nutrients release from the sediment, changes in the stratification structure of the reservoir according to future climate change are very important in terms of water quality and aquatic ecology management. This study was aimed to develop a data-driven inflow water temperature prediction model for Daecheong Reservoir (DR), and to predict future inflow water temperature and the stratification structure of DR considering future climate scenarios of Representative Concentration Pathways (RCP). The random forest (RF)regression model (NSE 0.97, RMSE 1.86℃, MAPE 9.45%) developed to predict the inflow temperature of DR adequately reproduced the statistics and variability of the observed water temperature. Future meteorological data for each RCP scenario predicted by the regional climate model (HadGEM3-RA) was input into RF model to predict the inflow water temperature, and a three-dimensional hydrodynamic model (AEM3D) was used to predict the change in the future (2018~2037, 2038~2057, 2058~2077, 2078~2097) stratification structure of DR due to climate change. As a result, the rates of increase in air temperature and inflow water temperature was 0.14~0.48℃/10year and 0.21~0.41℃/10year,respectively. As a result of seasonal analysis, in all scenarios except spring and winter in the RCP 2.6, the increase in inflow water temperature was statistically significant, and the increase rate was higher as the carbon reduction effort was weaker. The increase rate of the surface water temperature of the reservoir was in the range of 0.04~0.38℃/10year, and the stratification period was gradually increased in all scenarios. In particular, when the RCP 8.5 scenario is applied, the number of stratification days is expected to increase by about 24 days. These results were consistent with the results of previous studies that climate change strengthens the stratification intensity of lakes and reservoirs and prolonged the stratification period, and suggested that prolonged water temperature stratification could cause changes in the aquatic ecosystem, such as spatial expansion of the low-oxygen layer, an increase in sediment nutrient release, and changed in the dominant species of algae in the water body.

• Journal of Pharmaceutical Investigation
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• v.39 no.3
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• pp.185-199
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• 2009

Using a strain-controlled rheometer [Advanced Rheometric Expansion System (ARES)], the steady shear flow properties and the dynamic viscoelastic properties of $Antiphlamine-S^{(R)}$ lotion have been measured at $20^{\circ}C$ (storage temperature) and $37^{\circ}C$ (body temperature). In this article, the temperature dependence of the linear viscoelastic behavior was firstly reported from the experimental data obtained from a temperature-sweep test. The steady shear flow behavior was secondly reported and then the effect of shear rate on this behavior was discussed in detail. In addition, several inelastic-viscoplastic flow models including a yield stress parameter were employed to make a quantitative evaluation of the steady shear flow behavior, and then the applicability of these models was examined by calculating the various material parameters. The angular frequency dependence of the linear viscoelastic behavior was nextly explained and quantitatively predicted using a fractional derivative model. Finally, the strain amplitude dependence of the dynamic viscoelastic behavior was discussed in full to elucidate a nonlinear rheological behavior in large amplitude oscillatory shear flow fields. Main findings obtained from this study can be summarized as follows : (1) The linear viscoelastic behavior is almostly independent of temperature over a temperature range of $15{\sim}40^{circ}C$. (2) The steady shear viscosity is sharply decreased as an increase in shear rate, demonstrating a pronounced Non-Newtonian shear-thinning flow behavior. (3) The shear stress tends to approach a limiting constant value as a decrease in shear rate, exhibiting an existence of a yield stress. (4) The Herschel-Bulkley, Mizrahi-Berk and Heinz-Casson models are all applicable and have an equivalent validity to quantitatively describe the steady shear flow behavior of $Antiphlamine-S^{(R)}$ lotion whereas both the Bingham and Casson models do not give a good applicability. (5) In small amplitude oscillatory shear flow fields, the storage modulus is always greater than the loss modulus over an entire range of angular frequencies tested and both moduli show a slight dependence on angular frequency. This means that the linear viscoelastic behavior of $Antiphlamine-S^{(R)}$ lotion is dominated by an elastic nature rather than a viscous feature and that a gel-like structure is present in this system. (6) In large amplitude oscillatory shear flow fields, the storage modulus shows a nonlinear strain-thinning behavior at strain amplitude range larger than 10 % while the loss modulus exhibits a weak strain-overshoot behavior up to a strain amplitude of 50 % beyond which followed by a decrease in loss modulus with an increase in strain amplitude. (7) At sufficiently large strain amplitude range (${\gamma}_0$>100 %), the loss modulus is found to be greater than the storage modulus, indicating that a viscous property becomes superior to an elastic character in large shear deformations.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.12 no.3
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• pp.139-148
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• 2000

In this paper, the interaction of oblique incident waves with a bottom-mounted and fluid-filled flexible membrane structure is investigated in the frame of linear hydro-elastic theory. The static shape of a membrane structure containing the fluid of a specific density is initially unknown and must be calculated before the hydrodynamic analysis. To solve hydrodynamic problem, the fluid domain is divided into the inner and outer region. The inner solution based on discrete membrane dynamic model and simple-source distribution over the entire fluid boundaries is matched to the outer solution ba~ed on an eigenfunction expansion method. The numerical results were compared to a series of Ohyama's experimental results. The measured reflection and tran¬smission coefficients reasonably follow the trend of predicted values. Using the computer program developed, the performance of a bottom-mounted and fluid-filled flexible membrane strocture is tested with various system parameters (membrane shape, internal pressure, density ratio) and wave characteristics (wave frequencies, incident wave angle). It is found that a bottom-mounted and fluid-filled flexible membrane structure can be an effel;tive wave barrier if properly designed.

• Journal of Korean Society of Steel Construction
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• v.14 no.6
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• pp.735-746
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• 2002

The task of plastic theory is twofold: first, to set up relationships between stress and strain that adequately describe the observed plastic deformation of metals, and second, to develop techniques for using these relationships in studying of the mechanics of metal forming processes, and the anlaysis and design of structures. One of the major problems in the theory of plasticity is to describe the behavior of work-hardening materials in the plastic range for complex loading histories. This can be achieved by formulating constitutive laws either in the integral or differential forms. To adequately predict the response of steel members during cyclic loading, the hardening rule must account for the features of cyclic stress-strain behavior. Neithe of the basic isotropic and kinematic hardening rules is suitable for describing cyclic streess-strain behavior, although a kinematic hardening rule describes the nearly linear portions of the stabilized hystersis loops. There is also a limited expansion of the yield surface as predicted by the isotropic hardening rule. Strong ground motions or wind gusts affect the complex and nonproportional loading histories in the inelastic behavior of structues rather than the proportional loading. Nonproportional loading is defined as externally applied forces on the structure, with variable ratios during the entire loading history. This also includes the rate of time-dependency of the loads. For nonproportional loading histories, unloading may take place along a chord instead of the radius of the load surface. In such cases, the shape of the stress-strain curve has to be determined experimentally for all non-radial loading conditions. The plasticity models including two surface models ae surveyed based on a yield surface and a bound surface that represent a state of maximum stress. This paper is concerned with the improvement of a plasticity models of the two-surface type for structural steel. This is follwed by an overview of plasticity models on structural steel. Finally the need for further research is identified.

• Journal of the Korea Concrete Institute
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• v.19 no.3
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• pp.283-292
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• 2007

A research projects is currently being conducted to develop a nonlinear finite element analysis methods for predicting the structural behavior of reinforced concrete frame structures, exposed to fire. As part of this, reinforced concrete frames subjected to fire loads were analyzed using the nonlinear finite-element program DIANA. Two numerical steps are incorporated in this program. The first step carries out the nonlinear transient heat flow analysis associated with fire and the second step predicts the structural behavior of reinforced concrete frames subjected to the thermal histories predicted by first step. The complex features of structural behavior in fire conditions, such as thermal expansion, plasticity, cracking or crushing, and material properties changing with temperature are considered. A concrete material model based on nonlinear fracture mechanics to take cracking into account and plasticity models for concrete in compression and reinforcement steel were used. The material and analytical models developed in this paper are verified against the experimental data on simple reinforced concrete beams. The changes in thermal parameters are discussed from the point of view of changes of structure and chemical composition due to the high temperature exposure. Although, this study considers codes standard fire for reinforced concrete frame, any other time-temperature relationship can be easily incorporated.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.2
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• pp.187-194
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• 2012

The strength of particle-reinforced metal matrix composites is, in general, known to be increased by the geometrically necessary dislocations punched around a particle that form during cooling after consolidation because of coefficient of thermal expansion (CTE) mismatch between the particle and the matrix. An additional strength increase may also be observed, since another type of geometrically necessary dislocation can be formed during extensive deformation as a result of the strain gradient plasticity due to the elastic-plastic mismatch between the particle and the matrix. In this paper, the magnitudes of these two types of dislocations are calculated based on the dislocation plasticity. The dislocations are then converted to the respective strengths and allocated hierarchically to the matrix around the particle in the axisymmetric finite-element unit cell model. The proposed method is shown to be very effective by performing finite-element strength analysis of $SiC_p$/Al2124-T4 composites that included ductile failure in the matrix and particlematrix decohesion. The predicted results for different particle sizes and volume fractions show that the length scale effect of the particle size obviously affects the strength and failure behavior of the particle-reinforced metal matrix composites.

• Journal of the Korean Society for Railway
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• v.17 no.4
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• pp.270-280
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• 2014

In this study, to examine the causes of cracks in continuously reinforced concrete tracks (CRCTs) and the main factors affecting cracking, a field survey on the status of cracks and crack patterns in the Gyeong-bu high speed line was conducted, and the crack patterns of CRCT due to the temperature difference between the top of the slab (TCL) and the bottom of the subbase (HSB) and the drying shrinkage of concrete were predicted by a nonlinear finite element model considering the structure of CRCT. The results of the numerical analysis show that cracks will be developed at the interface between the sleeper and the TCL, and under the sleeper due to the temperature difference and concrete shrinkage. This corresponds well to the crack locations found in the field. Also, it is found that the most significant factors are the coefficient of thermal expansion with respect to the temperature difference, and the drying shrinkage strain with respect to shrinkage. According to the results, the reinforcement ratio should be carefully determined considering the structures of CRCT because the crack spacing is not always proportional to the reinforcement ratio due to the sleepers embedded in the TCL.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.