• Journal of the Korean Geotechnical Society
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• v.25 no.8
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• pp.33-45
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• 2009

The conventional single-core PBDs have been widely used in order to accelerate consolidation settlement of soft grounds. When using the single-core PBD in a thick clay deposit, a delay of consolidation may occur due to high confining pressure in the thick deposit and necking of drains. This study is to compare the performances of soil improvement by the single-core and double-core PBD installed at a site in Busan New Port which exhibits approximately a 40m-thick clay layer. An in-situ test program was performed at the test site where a set of the double-core PBDs and single-core PBDs were installed to compare the efficiency of each drain. In addition, the discharge capacity of each PBD has been measured using the modified Delft Test. A series of laboratory tests for estimating in-situ soil properties have also been performed in order to obtain input parameters for a numerical program ILLICON. The discharge capacity of the double-core PBD is higher than that of the single-core PBD in the modified Delft Test. However it is observed from the comparative in-situ test and numerical analysis that there is no difference in the performance of ground improvement between the two drain systems. This discrepancy comes from the fact that the amount of water released during consolidation in most common field conditions is much smaller than the capacity of even the single core PBD. And thus, considering actual field conditions, it can be concluded that the single-core PBD has enough discharge capacity even in the thick clay deposit such as this test site.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.3
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• pp.329-337
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• 2017

This paper proposes a search method that explores parameters C and σ values of support vector machines (SVM) to improve performance while maintaining search accuracy. A traditional grid search method requires tremendous computational times because it searches all available combinations of C and σ values to find optimal combinations which provide the best performance of SVM. To address this issue, this paper proposes a deep search method that reduces computational time. In the first stage, it divides C-σ- accurate metrics into four regions, searches a median value of each region, and then selects a point of the highest accurate value as a start point. In the second stage, the selected start points are re-divided into four regions, and then the highest accurate point is assigned as a new search point. In the third stage, after eight points near the search point. are explored and the highest accurate value is assigned as a new search point, corresponding points are divided into four parts and it calculates an accurate value. In the last stage, it is continued until an accurate metric value is the highest compared to the neighborhood point values. If it is not satisfied, it is repeated from the second stage with the input level value. Experimental results using normal and defect bearings show that the proposed deep search algorithm outperforms the conventional algorithms in terms of performance and search time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3D
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• pp.425-433
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• 2006

The presented work provided a predictive equation for dynamic modulus of hot mix asphalt, which showed higher reliability and more simplicity. Lots of test result by UTM at laboratory has been used to develop the precise predictive equation. Evaluation of dynamic modulus for 13mm and 19mm surface course and 25mm of base course of hot mix asphalt with granite aggregate and two asphalt binders (AP-3 and AP-5) were carried out. Superpave Level 1 Mix Design with gyrator compactor was adopted to determine the optimum asphalt binder content (OAC) and the measured ranges of OAC were between 5.1% and 5.4% for surface HMA, and around 4.2% for base HMA. The dynamic modulus and phase angle were determined by testing on UTM, with 5 different testing temperature (-10, 5, 20, 40, & $55^{\circ}C$) and 5 different loading frequencies (0.05, 0.1, 1, 10, 25 Hz). Using the measured dynamic modulus and phase angle, the input parameters of Sigmoidal function equation to represent the master curve were determined and these will be adopted in FEM analysis for asphalt pavements. The effect of each parameter for equation has been compared. Due to the limitation of laboratory tests, the reliability of predictive equation for dynamic modulus is around 80%.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.159-167
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• 2010

The purpose of this study is to get the adjusted radar rainfalls by ANN(Artificial Neural Network) method. In the case of radar rainfall, it has an advantage of spatial distribution characteristics of rainfall while point rainfall has an advantage at the point. Therefore we adjusted the radar rainfall by ANN method considering the advantages of two rainfalls of radar and point. This study constructed two ANN models of Model I and Model II for radar rainfall adjustment. We collected the three rainfall events and adjusted the radar rainfall for Anseong-cheon basin. The two events were inputted into the Modeland Model to derive the optimum parameters and the rest event was used for validation. The adjusted radar rainfalls by ANN method and the raw radar rainfall were used as the input data of ModClark model which is a semi-distributed model to simulate the runoff. As the results of the simulation, the runoff by raw radar rainfall were overestimated but the peak time and peak runoff from the adjusted rainfall by ANN were well fitted to the observed hydrograph.

• Journal of Korean Tunnelling and Underground Space Association
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• v.12 no.6
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• pp.429-441
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• 2010

In this study, fire resistance of a fireproof material sprayed upon an immersed tunnel was experimentally evaluated under $HC_{inc}$ and IS0834(duration of 4 hours) fire scenarios. Under $HC_{inc}$ fire scenario, the maximum inner temperatures of a concrete specimen at the depth of 0, 25 and 50 mm from the interface between the structure and the fire-proofing layer were $311^{\circ}C$, $194^{\circ}C$ and $142^{\circ}C$ respectively. Similarly, the corresponding maximum temperatures under IS0834 fire scenario were $332^{\circ}C$, $222^{\circ}C$ and $179^{\circ}C$ respectively. From the results, it was revealed that the two different fire scenarios assumed in this study have almost the same fire capacity as each other in the maximum temperature concept. In addition, a structural analysis of the immersed tunnel under $HC_{inc}$ fire scenario was carried out to verify the effects of the fireproof material on its structural stability. Material loss and deterioration of a concrete specimen without any fire-proofing measure was also experimentally evaluated to obtain input parameters for the structural analysis under such a severe fire scenario. From the results, it was confirmed that the application of fireproof measures to the immersed tunnel is essential for its structural stability even under a severe fire scenario.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6C
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• pp.259-266
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• 2009

The steel pipe of steel-concrete composite piles increases the pile strength and induces the ductile failure by constraining the deformation of the inner concrete. In this research, the numerical models and the related input parameters were analyzed to simulate the axial load-movement relations, which were obtained from the compression loading tests for the cylindrical specimens of the steel pipe, the concrete, and the steel-concrete composite. As the results, the behavior of the steel pipe was simulated by the von-Mises model and that of the concrete by the strain-softening model, which decreases cohesion and dilation angles as the function of plastic strains. In addition, the reinforcing bars in the concrete were simulated by applying the yielding moment and decreasing the sectional area of the bars. The applied numerical models properly simulated the yielding behavior and the reinforcement effect of the steel-concrete composite piles. The parametric study for the real-size piles showed that the material strength of the steel-concrete composite pile increased about 10% for the axial loading and about 20~45% for the horizontal loading due to the reinforcement effect by the surrounding steel pipe pile.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4A
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• pp.281-290
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• 2009

In this study, an analysis technique of hybrid girder considering nonlinearity of steel-concrete contact surface is presented. Steel-concrete hybrid girder shows partial-interaction behavior due to the deformation of shear connectors, slip and detachment at the interface, and cracks under the applied loads. Therefore, the partial-interaction approach becomes more reasonable. Contact surface is modeled by interface element and analyzed nonlinearly because of cost of time and effort to detailed model and analysis. Steel and Concrete are modeled considering non-linearity of materials. Material property of contact surface is obtained from push-out test and input to interface element. For the constitutive models, Drucker-Prager and smeared cracking model are used for concrete in compression and tension, respectively, and a von-Mises model is used for steel. This analysis technique is verified by comparing it with test results. Using verified analysis technique, various analyses are performed with different parameters such as nonlinear material property of interface element and prestress. The results are compared with linear analysis result and analysis result with the assumption of full-interaction.

• Journal of Environmental Health Sciences
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• v.49 no.6
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• pp.312-323
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• 2023

Background: Environmental concentrations of substances can be estimated by K-CHESAR based on main, industrial, and use categories (MC/IC/UC) and ECETOC TRA based on environmental or specific environmental categories (ERC or spERC). Objectives: Three different systems for estimating environmental concentrations were compared to figure out their order with possible reasons along with relationship of regional predicted environmental concentrations (PEC_regional) and final PEC_local for various uses of a substance. Methods: Typical uses of the case substance and their corresponding ERCs were selected from the webpage of the European Chemical Agency. Proper MC/IC/UC and spERC were assigned to each ERC. Emission fractions were compared for each assessment code from the available database. PECs were calculated by three estimating systems: K-CHESAR using MC/IC/UC, ECETOC TRA using ERC, and ECETOC TRA using spERC with their default values for input parameters. Percentage of PEC_regional to PEC_local were manually calculated for each use. Results: Emission factors decreased in the order of ERC > MC/IC/UC > spERC. Values of the final PEC_local derived as sum of PEC_regional and Clocal decreased in the order of calculations using ECETOC TRA-ERC>KCHESAR with MC/IC/UC>ECETOC TRA-spERC for all environmental media. Percentages of PEC_{regional,water} to PEC_local,water ranged from 0 to 10.3% in industrial uses calculated with MC/IC/UC and ERC but 96.3 to 100% in wide dispersive uses of ERC and spERC where values of C_local,water are estimated to be very low. Conclusions: ECETOC TRA generated the most refined PNEC values with spERC and the least with ERC, while K-CHESAR with MC/IC/UC generated values between the two results. The ratio of PEC_regional to PEC_local can be a good measure for performing suitable estimation of PNECs according to use.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.5
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• pp.1-18
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• 2023

With the recent developments in big data and deep learning, a variety of traffic information is collected widely and used for traffic operations. In particular, long short-term memory (LSTM) is used in the field of traffic information prediction with time series characteristics. Since trends, seasons, and cycles differ due to the nature of time series data input for an LSTM, a trial-and-error method based on characteristics of the data is essential for prediction models based on time series data in order to find hyperparameters. If a methodology is established to find suitable hyperparameters, it is possible to reduce the time spent in constructing high-accuracy models. Therefore, in this study, a traffic information prediction model is developed based on highway vehicle detection system (VDS) data and LSTM, and an impact assessment is conducted through changes in the LSTM evaluation indicators for each hyperparameter. In addition, a methodology for finding hyperparameters suitable for predicting highway traffic information in the transportation field is presented.