• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.38 no.2
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• pp.164-171
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• 2002

In this study, a mass-spring model is used to dynamically describe and calculate the shape and movement of a mid-water trawl system. This mathematical model theorizes that the factors constituting the system are the material points and the external forces such as hydrodynamic load, gravity, and buoyancy act on these material points. In addition, it surmises that these material points are connected to each other by springs, the springs do not have any mass, and the internal force acts on these springs. The non-linear differential equations are implicitly integrated with time for guaranteeing a stable solution. The dynamic simulation by the mass-spring model shows the status of the gear such as fishing gear depth, distance between doors, shape of the gear, and tension of each line. It depends on the parameters such as towing force, warp length, force of a sinker, buoyancy of a float, type of door and netting materials. The validity of the model is verified by comparing simulation motions of a trawl system obtained from computed values to those from an actual experiment.

• Journal of the Korean Society of Hazard Mitigation
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• v.5 no.2 s.17
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• pp.9-16
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• 2005

This paper presents the use of an Artificial Neural Network (ANN) as a viable means of forecasting Inundation Damage Area (IDA) in many watersheds. In order to develop the forecasting model with various environmental factors, we selected 108 watershed areas in South Korea and collected 49 damage data sets from 1990 to 2000, of which each set is composed of 27 parameters including the IDA, rainfall amount, and land use. After successful training processes of the ANN, a good agreement (R=0.92) is obtained (under present conditions) between the measured values of the IDA and those predicted by the developed ANN using the remaining 26 data sets as input parameters. The results indicate that the inundation damage is affected by not only meteorological information such as the rainfall amount, but also various environmental characteristics of the watersheds. So, the ANN proves its present ability to predict the IDA caused by an event of complex factors in a specific watershed area using accumulated temporal-spatial information, and it also shows a potential capability to handle complex non-linear dynamic phenomena of environmental changes. In this light, the ANN can be further harnessed to estimate the importance of certain input parameters to an output (e.g., the IDA in this study), quantify the significance of parameters involved in pre-existing models, and contribute to the presumption, selection, and calibration of input parameters of conventional models.

• Computers and Concrete
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• v.22 no.3
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• pp.337-353
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• 2018

The present study focuses on examining the structural behaviour of steel-fibre-reinforced concrete (SFRC) beams under high rates of loading largely associated with impact problems. Fibres are added to the concrete mix to enhance ductility and energy absorption, which is important for impact-resistant design. A simple, yet practical non-linear finite-element analysis (NLFEA) model was used in the present study. Experimental static and impact tests were also carried out on beams spanning 1.3 meter with weights dropped from heights of 1.5 m and 2.5 m, respectively. The numerical model realistically describes the fully-brittle tensile behaviour of plain concrete as well as the contribution of steel fibres to the post-cracking response (the latter was allowed for by conveniently adjusting the constitutive relations for plain concrete, mainly in uniaxial tension). Suitable material relations (describing compression, tension and shear) were selected for SFRC and incorporated into ABAQUS software Brittle Cracking concrete model. A more complex model (i.e., the Damaged Plasticity concrete model in ABAQUS) was also considered and it was found that the seemingly simple (but fundamental) Brittle Cracking model yielded reliable results. Published data obtained from drop-weight experimental tests on RC and SFRC beams indicates that there is an increase in the maximum load recorded (compared to the corresponding static one) and a reduction in the portion of the beam span reacting to the impact load. However, there is considerable scatter and the specimens were often tested to complete destruction and thus yielding post-failure characteristics of little design value and making it difficult to pinpoint the actual load-carrying capacity and identify the associated true ultimate limit state (ULS). To address this, dynamic NLFEA was employed and the impact load applied was reduced gradually and applied in pulses to pinpoint the actual failure point. Different case studies were considered covering impact loading responses at both the material and structural levels as well as comparisons between RC and SFRC specimens. Steel fibres were found to increase the load-carrying capacity and deformability by offering better control over the cracking process concrete undergoes and allowing the impact energy to be absorbed more effectively compared to conventional RC members. This is useful for impact-resistant design of SFRC beams.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.6
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• pp.523-530
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• 2017

In this study, a method of probabilistic evaluation of the performance point of the structure obtained by capacity spectrum method (CSM) is presented. The performance point of the 4-story and 1-bay steel structure was determined by using CSM according to ATC-40. In order to analyze whether the demand spectrums exceed the performance limit of the structure, the limit displacements are derived for the performance limit of the structure defined from the plastic deformation angle of the structural member. In addition, by selecting a total of 30 artificial seismic wave having the response spectrum similar to the design response spectrum, the fragility curves were derived by examining whether the response spectrum obtained from the artificial seismic wave were exceeded each performance limit according to the spectral acceleration. The maximum likelihood method was used to derive the fragility curve using observed excess probabilities. It has been confirmed that there exists a probability that the response acceleration value of the design response spectrum corresponding to each performance limit exceeds the performance limit. This method has a merit that the stochastic evaluation can be performed considering the uncertainty of the seismic waves with respect to the performance point of the structure, and the analysis time can be shortened because the incremental dynamic analysis (IDA) is not necessary.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.9-17
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• 2021

Curved beams are increasingly used in buildings, vehicles, ships, and aircraft, which has resulted in considerable effort being directed toward developing an accurate method for analyzing the dynamic behavior of such structures. The stability behavior of elastic circular arches has been the subject of a large number of investigations. One of the efficient procedures for the solution of ordinary differential equations or partial differential equations is the differential quadrature method DQM. This method has been applied to a large number of cases to overcome the difficulties of the complex computer algorithms, as well as excessive use of storage due to conditions of non-linear geometries, loadings, or material properties. This study uses DQM to analyze the in-plane vibration of the circular arches considering the effects of midsurface extension and rotatory inertia. Fundamental frequency parameters are calculated for the member with various parameter ratios, boundary conditions, and opening angles. The solutions from DQM are compared with exact solutions or other numerical solutions for cases in which they are available and given to analyze the effects of midsurface extension and rotatory inertia on the frequency parameters of the circular arches.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.597-603
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• 2006

The purpose of this study is to improve the short term rainfall forecast skill using neural network model that can deal with the non-linear behavior between satellite data and ground observation, and minimize the flood damage. To overcome the geographical limitation of Korean peninsula and get the long forecast lead time of 3 to 6 hour, the developed rainfall forecast model took satellite imageries and wide range AWS data. The architecture of neural network model is a multi-layer neural network which consists of one input layer, one hidden layer, and one output layer. Neural network is trained using a momentum back propagation algorithm. Flood was estimated using rainfall forecasts. We developed a dynamic flood inundation model which is associated with 1-dimensional flood routing model. Therefore the model can forecast flood aspect in a protected lowland by levee failure of river. In the case of multiple levee breaks at main stream and tributaries, the developed flood inundation model can estimate flood level in a river and inundation level and area in a protected lowland simultaneously.

• Advances in nano research
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• v.16 no.4
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• pp.325-340
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• 2024

There are several novel uses for dispersing many nanoparticles into a conventional fluid, including dynamic sealing, damping, heat dissipation, microfluidics, and more. Therefore, melting heat and mass transfer characteristics of a 3-D MHD Hybrid Nanofluid flow over a rotating disc with presenting dufour and soret effects are assessed numerically in this study. In this instance, we investigated both ferric sulfate and molybdenum disulfide as nanoparticles suspended within base fluid water. The governing partial differential equations are transformed into linked higher-order non-linear ordinary differential equations by the local similarity transformation. The collection of these deduced equations is then resolved using a Chebyshev spectral collocation-based algorithm built into the Mathematica software. To demonstrate how different instances of hybrid/ nanofluid are impacted by changes in temperature, velocity, and the distribution of nanoparticle concentration, examples of graphical and numerical data are given. For many values of the material parameters, the computational findings are shown. Simulations conducted for different physical parameters in the model show that adding hybrid nanoparticle to the fluid mixture increases heat transfer in comparison to simple nanofluids. It has been identified that hybrid nanoparticles, as opposed to single-type nanoparticles, need to be taken into consideration to create an effective thermal system. Furthermore, porosity lowers the velocities of simple and hybrid nanofluids in both cases. Additionally, results show that the drag force from skin friction causes the nanoparticle fluid to travel more slowly than the hybrid nanoparticle fluid. The findings also demonstrate that suction factors like magnetic and porosity parameters, as well as nanoparticles, raise the skin friction coefficient. Furthermore, It indicates that the outcomes from different flow scenarios correlate and are in strong agreement with the findings from the published literature. Bar chart depictions are altered by changes in flow rates. Moreover, the results confirm doctors' views to prescribe hybrid nanoparticle and particle nanoparticle contents for achalasia patients and also those who suffer from esophageal stricture and tumors. The results of this study can also be applied to the energy generated by the melting disc surface, which has a variety of industrial uses. These include, but are not limited to, the preparation of semiconductor materials, the solidification of magma, the melting of permafrost, and the refreezing of frozen land.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.923-932
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• 2024

The purpose of this study is to explore the direction of teaching and learning in the era of science and technology. The conclusion is as follows: From the perspective of new materialism, the direction of teaching and learning first emphasizes the importance of learning experience. Second, a teaching method that emphasizes giving meaning is needed. Third, there is a need to consider various perspectives and backgrounds. The direction of teaching and learning in the era of phenomenology is, first, the meaning and meaning of learning is important. Second, it emphasizes the role of independent learners. Third, diverse experiences must be integrated. Fourth, a flexible approach is needed depending on the environment and situation. Rather than a fixed teaching method, it is necessary to use a variety of teaching strategies according to the situation and needs of the learner. From the perspective of actor network theory, the direction of teaching and learning needs to first embrace a variety of actors. Second, there is a need to understand the role of the mediator actor. Third, there is a need to emphasize the dynamic aspect of the network. Fourth, there is a need to emphasize interaction and giving meaning. Fifth, there is a need to strengthen interaction with technology. Sixth, there is a need to create a non-linear and open learning environment. The direction of society and teaching and learning presented in Trend Korea 2024 considers time as a precious resource in a divided society, and teaching and learning must adapt and develop in a useful direction in a changing environment.

• Korean Journal of Biological Psychiatry
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• v.5 no.1
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• pp.95-101
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• 1998

The changes of electroencephalogram(EEG) in patients with dementia of Alzheimer's type are most commonly studied by analyzing power or magnitude in traditionally defined frequency bands. However because of the absence of an identified metric which quantifies the complex amount of information, there are many limitations in using such a linear method. According to the chaos theory, irregular signals of EEG can be also resulted from low dimensional deterministic chaos. Chaotic nonlinear dynamics in the EEG can be studied by calculating the largest Lyapunov exponent($L_1$). The authors have analyzed EEG epochs from three patients with dementia of Alzheimer's type and three matched control subjects. The largest $L_1$ is calculated from EEG epochs consisting of 16,384 data points per channel in 15 channels. The results showed that patients with dementia of Alzheimer's type had significantly lower $L_1$ than non-demented controls on 8 channels. Topographic analysis showed that the $L_1$ were significantly lower in patients with Alzheimer's disease on all the frontal, temporal, central, and occipital head regions. These results show that brains of patients with dementia of Alzheimer's type have a decreased chaotic quality of electrophysiological behavior. We conclude that the nonlinear analysis such as calculating the $L_1$ can be a promising tool for detecting relative changes in the complexity of brain dynamics.