• Environmental Engineering Research
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• v.21 no.2
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• pp.203-210
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• 2016

Hybrid materials were obtained modifying the bentonite (BC) and local clay (LC) using hexadecyltrimethylammonium bromide (HDTMA) or the clay were pillared with aluminum followed by modification with HDTMA. The materials were characterized by the SEM, FT-IR and XRD analytical tools. The batch reactor data implied that the uptake of $17{\beta}$-estradiol (E2) by the hybrid materials showed very high uptake at the neutral pH region. However, at higher and lower pH conditions, slightly less uptake of E2 was occurred. The uptake of E2 was insignificantly affected changing the sorptive concentration from 1.0 to 10.0 mg/L and the background electrolyte (NaCl) concentrations from 0.0001 to 0.1 mol/L. Moreover, the sorption of E2 by these hybrid materials was fairly efficient since within 30 mins of contact time, an apparent equilibrium between solid and solution was achieved, and the data was best fitted to the PSO (pseudo-second order) and FL-PSO (Fractal-like-pseudo second order) kinetic models compared to the PFO (pseudo-first order) model. The fixed-bed column results showed that relatively high breakthrough volume was obtained for the attenuation of E2 using these hybrid materials, and the loading capacity of E2 was estimated to be 75.984, 63.757, 58.965 and 49.746 mg/g for the solids BCH, BCAH, LCH and LCAH, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.2
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• pp.82-95
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• 1998

In this paper, we propose a text-independent speaker identification system which has a classifier composed of two parts; to calculate the degree of likeness of each speech frame and to select the most probable speaker from the entire speech duration. The first part is realized using RBFN which is selforganized through learning and in the second part the speaker is determined using a con-tbination of MAXNET and delayed summings. And we use features from linear speech production model and features from fractal geometry. Closed-set speaker identification experiments on 13 male homogeneous speakers show that the proposed techniques can achieve the identification ratio of 100% as the number of delays increases.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.634-644
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• 2000

Fatigue crack growth and life is estimated by various fracture mechanical parameters but affected by load, material and environment. Fatigue character of component without surface notch cannot be e valuated by above-mentioned parameters due to microstructure of in-service material. Single fracture mechanical parameter or nondestructive parameter cannot predict fatigue damage in arbitrary boundary condition but multiple fracture mechanical parameters or nondestructive parameters can Fatigue crack growth modelling with three point representation scheme uses this merit but has limit on real-time monitoring. Therefore, this study shows fatigue damage model using backpropagatior. neural networks on the basis of X-ray half breadth ratio B/$B_o$ fractal dimension $D_f$ and fracture mechanical parameters can predict fatigue crack growth rate da/dN and cycle ratioN/$N_f$ at the same time within engineering estimated mean error(5%).

• Tribology and Lubricants
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• v.34 no.1
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• pp.1-8
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• 2018

This paper presents the analysis of friction master curves for a sliding elastomer on rough granite. The hysteresis friction is calculated using an analytical model that considers the energy spent during the local deformation of the rubber due to surface asperities. The adhesion friction is also considered for dry friction prediction. The viscoelastic modulus of the rubber compound and the large-strain effective modulus are obtained from dynamic mechanical analysis (DMA). We accurately demonstrate the large strain of rubber that contacts with road substrate using the GW theory. We found that the rubber block deforms approximately to 40% strain. In addition, the viscoelastic master curve considering nonlinearity (at 40% strain) is derived based on the above finding. As viscoelasticity strongly depends on temperature, it can be assumed that the influence of velocity on friction is connected to the viscoelastic shift factors gained from DMA using the time-temperature superposition. In this study, we apply these shift factors to measure friction on dry granite over a velocity range for various temperatures. The measurements are compared to simulated hysteresis and adhesion friction using the Kluppel friction theory. Although friction results in the low-speed band match well with the simulation results, there are differences in the predicted and experimental results as the velocity increases. Thus, additional research is required for a more precise explanation of the viscoelastic material properties for better prediction of rubber friction characteristics.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.6
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• pp.965-973
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• 2021

The behavior of angelfish Pterophyllum scalare exposed to low and high temperatures was monitored by video tracking, and information such as the initial speed, changes in speed, and locations of the fish in the tank were analyzed. The water temperature was raised from 26℃ to 36℃ or lowered from 26℃ to 16℃ for 4 h. The control group was maintained at 26℃ for 8 h. The experiment was repeated five times for each group. Machine learning analysis comprising a long short-term memory model was used to train and test the behavioral data (80 s) after pre-processing. Results showed that when the water temperature changed to 36℃ or 16℃, the average speed, changes in speed and fractal dimension value were significantly lower than those in the control group. Machine learning analysis revealed that the accuracy of 80-s video footage data was 87.4%. The machine learning used in this study could distinguish between the optimal temperature group and changing temperature groups with specificity and sensitivity percentages of 86.9% and 87.4%, respectively. Therefore, video tracking technology can be used to effectively analyze fish behavior. In addition, it can be used as an early warning system for fish health in aquariums and fish farms.

• Smart Structures and Systems
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• v.30 no.4
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• pp.421-432
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• 2022

Regarding the high significance of correct pan evaporation modeling, this study introduces two novel neuro-metaheuristic approaches to improve the accuracy of prediction for this parameter. Vortex search algorithms (VSA), sunflower optimization (SFO), and stochastic fractal search (SFS) are integrated with a multilayer perceptron neural network to create the VSA-MLPNN, SFO-MLPNN, and SFS-MLPNN hybrids. The climate data of Arcata-Eureka station (operated by the US environmental protection agency) belonging to the years 1986-1989 and the year 1990 are used for training and testing the models, respectively. Trying different configurations revealed that the best performance of the VSA, SFO, and SFS is obtained for the population size of 400, 300, and 100, respectively. The results were compared with a conventionally trained MLPNN to examine the effect of the metaheuristic algorithms. Overall, all four models presented a very reliable simulation. However, the SFS-MLPNN (mean absolute error, MAE = 0.0997 and Pearson correlation coefficient, R_P = 0.9957) was the most accurate model, followed by the VSA-MLPNN (MAE = 0.1058 and R_P = 0.9945), conventional MLPNN (MAE = 0.1062 and R_P = 0.9944), and SFO-MLPNN (MAE = 0.1305 and R_P = 0.9914). The findings indicated that employing the VSA and SFS results in improving the accuracy of the neural network in the prediction of pan evaporation. Hence, the suggested models are recommended for future practical applications.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.483-492
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• 2007

Time series of conventional prediction techniques uses the model which is generated from the training step. This model is applied to new input data without any change. If this model is applied directly to stream data, the rate of prediction accuracy will be decreased. This paper proposes an stream data prediction technique using sliding window and regression. This technique considers the characteristic of time series which may be changed over time. It is composed of two steps. The first step executes a fractional process for applying input data to the regression model. The second step updates the model by using its information as new data. Additionally, the model is maintained by only recent data in a queue. This approach has the following two advantages. It maintains the minimum information of the model by using a matrix, so space complexity is reduced. Moreover, it prevents the increment of error rate by updating the model over time. Accuracy rate of the proposed method is measured by RME(Relative Mean Error) and RMSE(Root Mean Square Error). The results of stream data prediction experiment are performed by the proposed technique IMQR(Incremental Multiple Quadratic Regression) is more efficient than those of MLR(Multiple Linear Regression) and SVR(Support Vector Regression).

• 한국해양학회지
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• v.29 no.4
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• pp.392-401
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• 1994

We examined the temporal characteristics of the oceanic eddy diffusion at 5 coastal regions of Korea by measuring the separation distances of multiple drifters released simultaneously at the same by the GPS and Decca transponder system. The observed variance of separation distance, for the time scales from minutes to hours, is proportional to t/SUP m/ with scaling exponent m between 1.2 and 2.0. The observed Lagrangian trajectories of drifters show fractal characteristics instead of random walk or Brown motion. As an effort toward a development of a realistic model of the oceanic eddy diffusion, we simulated the Lagrangian trajectories of drifters by fractional Brown motion (FBM) model. The observed variances of drifter separations can be generated by the FBM process provided the Hurst exponent is the same as the observed one. We further showed that the observed power law in the variance of drifter separations cannot be simulated with an ordinary Brown motion or random walk process.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.9 no.2
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• pp.75-80
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• 2023

Modelling the damage to DNA molecules by ionizing radiation plays a crucial part in predicting the biological effects of any form of radiation therapy, but the creation of accurate damage models remains scientifically challenging. This study evaluated the frequency and severity of DNA strand breaks caused by direct and indirect radiation effects using the Geant4 DNA simulation toolkit. The DNA itself was represented as a continuous fractal Hilbert curve with a total length of approximately 6.4 Gbp, consisting of straight and twisted chromatin sections placed inside a simplified model of a human fibroblast cell. Using At-211 and Ac-225, both alpha-emitting radionuclides employed under assumption of radiopharmaceutical treatment, the results were compared to those from external irradiation with 1.5 MeV gamma rays. For each Gy of absorbed dose, the strand break yields were 103 ± 10 SSBs/Gbp and 15 ± 4 DSBs/Gbp for At-211, 96 ± 10 SSBs/Gbp and 15 ± 4 DSBs/Gbp for Ac-225, as well as 198 ± 14 SSBs/Gbp and 7 ± 3 DSBs/Gbp for the gamma rays. Thus, the radionuclides exhibited more than double the incidence of DSBs at the expense of SSBs compared to the gamma radiation. By demonstrating the feasibility of adapting the Geant4 DNA toolkit for in silico studies of the radiobiological effects of therapeutic radiopharmaceuticals at the DNA level, this is the first step towards the development of a comprehensive simulation model for determining the relative biological effectiveness of radiopharmaceuticals.

• Journal of the Korean association of regional geographers
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• v.20 no.1
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• pp.70-91
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• 2014

There have been a lot of efforts to adapt climate change around the world, and Korea is no exception. The low carbon green cities for overseas have had many different forms through their own special models and strategies. Korea needs a model and strategy of Korean low carbon green city, which is suitable for Korea climate and topography. This study pays attention to the Pungsu, which is Korean traditional thinking system for space, and examines the way for selecting locations and space planning to create the Korean low carbon green city through the contemporary interpretation of the Pungsu. For this purpose, first of all, this study makes efforts for the contemporary interpretation of the past Pungsu theory from the modern city's perspective, through understanding the difference between the Korea's historic villages(cities) and the modern cities. Based on the contemporary interpretation of the Pungsu theory, this study finds ways of application the system on selecting locations and space planning in the Pungsu theory to create the Korean low carbon green city.