• Computers and Concrete
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• v.32 no.6
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• pp.607-613
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• 2023

The objective of this work is to determine the compressive strength of geopolymer concrete utilizing four distinct machine learning approaches. These techniques are known as gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized trees (XRT), and deep learning (DL). Experimentation is performed to collect the data that is then utilized for training the models. Compressive strength is the response variable, whereas curing days, curing temperature, silica fume, and nanosilica concentration are the different input parameters that are taken into consideration. Several kinds of errors, including root mean square error (RMSE), coefficient of correlation (CC), variance account for (VAF), RMSE to observation's standard deviation ratio (RSR), and Nash-Sutcliffe effectiveness (NSE), were computed to determine the effectiveness of each algorithm. It was observed that, among all the models that were investigated, the GBM is the surrogate model that can predict the compressive strength of the geopolymer concrete with the highest degree of precision.

• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.21-33
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• 2019

This study performed to simulate the watershed storm runoff using data of S-band dual-polarization radar rain, GPM (Global Precipitation Mission) satellite rain, and observed rainfall at 21 ground stations operated by KMA (Korea Meteorological Administration) respectively. For the 3 water level gauge stations (Sancheong, Changchon, and Namgang) of NamgangDam watershed ($2,293km^2$), the KIMSTORM2 (KIneMatic wave STOrm Runoff Model2) was applied and calibrated with parameters of initial soil moisture contents, Manning's roughness of overland and stream to the event of typhoon CHABA (82 mm in watershed aveprage) in $5^{th}$ October 2016. The radar and GPM data was corrected with CM (Conditional Merging) method such as CM-corrected Radar and CM-corrected GPM. The CM has been used for accurate rainfall estimation in water resources and meteorological field and the method combined measured ground rainfall and spatial data such as radar and satellite images by the kriging interpolation technique. For the CM-corrected Radar and CM-corrected GPM data application, the determination coefficient ($R^2$) was 0.96 respectively. The Nash-Sutcliffe efficiency (NSE) was 0.96 and the Volume Conservation Index (VCI) was 1.03 respectively. The CM-corrected data of Radar and GPM showed good results for the CHABA peak runoff and runoff volume simulation and improved all of $R^2$, NSE, and VCI comparing with the original data application. Thus, we need to use and apply the radar and satellite data to monitor the flood within the watershed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1817-1836
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• 2020

Network convergence is considered as one of the key solutions to the problem of achieving future high-capacity and reliable communications. This approach overcomes the limitations of separate wireless technologies. Efficient interface selection is one of the most important issues in convergence networks. This paper solves the problem faced by users of selecting the most appropriate interface in the heterogeneous radio-access network (RAN) environment. Our proposed scheme combines a hierarchical evaluation of networks and game theory to solve the network-selection problem. Instead, of considering a fixed weight system while ranking the networks, the proposed scheme considers the service requirements, as well as static and dynamic network attributes. The best network is selected for a particular service request. To establish a hierarchy among the network-evaluation criteria for service requests, an analytical hierarchy process (AHP) is used. To determine the optimum network selection, the network hierarchy is combined with game theory. AHP attains the network hierarchy. The weights of different access networks for a service are calculated. It is performed by combining AHP scores considering user's experienced static network attributes and dynamic radio parameters. This paper provides a strategic game. In this game, the network scores of service requests for various RANs and the user's willingness to pay for these services are used to model a network-versus-user game. The Nash equilibria signify those access networks that are chosen by individual user and result maximum payoff. The examples for the interface selection illustrate the effectiveness of the proposed scheme.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.463-474
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• 2004

Game theory is a method of mathematical analysis developed to study the decision making process. In 1928, Von Neumann mathematically proved that every two-person, zero-sum game with many pure finite strategies for each player is deterministic. In the early 50's, Nash presented another concept as the basis for a generalization of Von Neumann's theorem. Another central achievement of game theory is the introduction of evolutionary game theory, by which agents can play optimal strategies in the absence of rationality. Through the process of Darwinian selection, a population of agents can evolve to an Evolutionary Stable Strategy (ESS) as introduced by Maynard Smith in 1982. Keeping pace with these game theoretical studies, the first computer simulation of coevolution was tried out by Hillis. Moreover, Kauffman proposed the NK model to analyze coevolutionary dynamics between different species. He showed how coevolutionary phenomenon reaches static states and that these states are either Nash equilibrium or ESS in game theory. Since studies concerning coevolutionary phenomenon were initiated, there have been numerous other researchers who have developed coevolutionary algorithms. In this paper we propose a new coevolutionary algorithm named Game theory based Coevolutionary Algorithm (GCEA) and we confirm that this algorithm can be a solution of evolutionary problems by searching the ESS. To evaluate this newly designed approach, we solve several test Multiobjective Optimization Problems (MOPs). From the results of these evaluations, we confirm that evolutionary game can be embodied by the coevolutionary algorithm and analyze the optimization performance of our algorithm by comparing the performance of our algorithm with that of other evolutionary optimization algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5269-5286
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• 2018

Task scheduling is one of the key issues in improving system performance and optimizing resource management in cloud computing environment. In order to provide appropriate services for heterogeneous users, we propose a novel task scheduling strategy with service differentiation, in which the delay sensitive tasks are assigned to the rapid cloud with high-speed processing, whereas the fault sensitive tasks are assigned to the reliable cloud with service restoration. Considering that a user can receive service from either local SaaS (Software as a Service) servers or public IaaS (Infrastructure as a Service) cloud, we establish a hybrid queueing network based system model. With the assumption of Poisson arriving process, we analyze the system model in steady state. Moreover, we derive the performance measures in terms of average response time of the delay sensitive tasks and utilization of VMs (Virtual Machines) in reliable cloud. We provide experimental results to validate the proposed strategy and the system model. Furthermore, we investigate the Nash equilibrium behavior and the social optimization behavior of the delay sensitive tasks. Finally, we carry out an improved intelligent searching algorithm to obtain the optimal arrival rate of total tasks and present a pricing policy for the delay sensitive tasks.

• Water for future
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• v.19 no.1
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• pp.65-74
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• 1986

The purpose of this study was to investigate the applicability of Single Linear Reservoir (SLR) model for runoff computations of small river basins in Korea. In the existing watershed flood routing methods the storage coefficient(K), which is the dominant parameter in the model, has been proposed to be computed in terms of the wqtershed characteristics. However, in the prsent study, the rainfall characteristics in addition to the watershed characteristics were taken into account in the multiple regression analysis for more accurate estimation of storage coefficient. The parameters finally adopted for the regressions were the drainge are, mean stream slope of the watershed, and the duration and total dffective amount of rainfalls. To verify the applicability of SLR model the computed results by SLR model with K determined by the regression equation were compared with the observed gydrographs, and also with those by other runoff computation methods; namely, the Clark method, nakayasu's synthetic unit hydrograph method and Nash model. The results showed that the present zSLR model gave the best results among these methods in the case of small river basins, but for the whatersheds with significant draingage area the Clark method gave the best results. However, it was speculated that the SLR model could also be accurately applied for flood compuatation in large wagersheds provided that the regression for storage coefficients were made with the actual data obtained in the large river basins.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.41-47
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• 2024

In this paper, we deal with a game theoretic problem to explore interactions between evasive Artificial Intelligence (AI) malware and detectors in Internet of Things (IoT). Evasive AI malware is defined as malware having capability of eluding detection by exploiting artificial intelligence such as machine learning and deep leaning. Detectors are defined as IoT devices participating in detection of evasive AI malware in IoT. They can be separated into two groups such that one group of detectors can be armed with detection capability powered by AI, the other group cannot be armed with it. Evasive AI malware can take three strategies of Non-attack, Non-AI attack, AI attack. To cope with these strategies of evasive AI malware, detector can adopt three strategies of Non-defense, Non-AI defense, AI defense. We formulate a Bayesian game theoretic model with these strategies employed by evasive AI malware and detector. We derive pure strategy Bayesian Nash Equilibria in a single stage game from the formulated Bayesian game theoretic model. Our devised work is useful in the sense that it can be used as a basic game theoretic model for developing AI malware detection schemes.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.457-460
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• 2002

Development and application of nonpoint pollutant source model need pertinent runoff simulation for expecting good simulation result of yield of nonpont pollutant and it's move. this study purpose was compare to runoff height among Observed of Regression, HSPF and SWAT in hukchun basis loacated Gyeonggi province yangpeong-gun in two years($1998{\sim}1999$). Result, runoff height were Regression, SWAT, HSPF is 2578.96, 2526.44, 2547.21mm respectively, Nash-Schutcliff' simulation efficiency, compare to observed, was 70.22, 73.71% respectively so two simulation run off height was pertinent. If Regression method use excess observed arrange, it include error. so it's importance using pertinent arrange of observed runoff height.

• Journal of the Korean Operations Research and Management Science Society
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• v.22 no.4
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• pp.71-80
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• 1997

In this paper, we consider with a market pioneering game among symmetric firms in highly competitive situation. To describe the puzzling situation of timing competition, we construct a dynamic game model and explore the equilibrium solution. As a result, we find a subgame perfect mixed strategy Nash equilibrium conceptually defined by 't$_{0}$ + .elsilon. equilibrium'. Our major finding s include : i) market entry will be occurred in sequential manner even though the condition of each firm is symmetric ii) the optimal timing of market pioneering will be advanced until almost all of the monopolist's profit is dissipated, iii) as the market position of the pioneer is stronger, the timings of the pioneer and the follower are separated, iv) and as the slope of the profit flow is steeper, the entry timing of the two players will be pooled together.

• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.985-997
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• 2020

SWAT (Soil and Water Assessment Tool)-MODFLOW (Modular Groundwater Flow) is a coupled model that linking semi-distributed watershed hydrology with fully-distributed groundwater behavior. In this study, the groundwater simulation results of SWAT and SWAT-MODFLOW were compared for Bokhacheon watershed in Namhan river basin. The models were calibrated and validated with 9 years (2009~2017) daily streamflow (Q) data of Heungcheon (HC) water level gauge station and the daily groundwater level observation data of Yulheon (YH). For SWAT, the groundwater parameters of GW_DELAY, GWQMN, and ALPHA_BF affecting baseflow and recession phase were treated. The SWAT results showed the coefficient of determination (R²) of 0.7 and Nash-Sutcliffe model efficiencies (NES_Q, NSE_inQ) for Q and 1/Q with 0.73 and -0.1 respectively. For SWAT-MODFLOW, the spatio-temporal aquifer hydraulic conductivity (K, m/day), specific storage (S_s, 1/m), and specific yield (S_y) were applied. The SWAT-MODFLOW showed R², NSE_Q, and NSE_inQ of 0.69, 0.74, and 0.51 respectively. The SWAT-MODFLOW considerably enhanced the low flow simulation with the help of aquifer physical information. The total streamflow of SWAT and SWAT-MODFLOW were 718.6 mm and 854.9 mm occupying baseflow of 342.9 mm and 423.5 mm respectively.