• Smart Structures and Systems
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• v.24 no.1
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• pp.141-155
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• 2019

This work evaluates the influence of negative stiffness on the performances of various vehicle suspension systems, and proposes a re-centering negative stiffness device (NSD). The re-centering NSD consists of a passive magnetic negative stiffness spring and a positioning shaft with a re-centering function. The former produces negative stiffness control forces, and the latter prevents the amplification of static spring deflection. The numerical simulations reveal that negative stiffness can improve the ride comfort of a vehicle without affecting its road holding abilities for either passive or semi-active suspension systems. In general, the improvement degree of ride comfort increases as negative stiffness increases. For passive suspension system, negative stiffness brings in negative stiffness feature in the control forces, which is helpful for the ride comfort of a vehicle. For semi-active suspensions, negative stiffness can alleviate the impact of clipped damping in semi-active dampers, and thus the ride comfort of a vehicle can be improved.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.156-166
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• 2020

In this paper, to improve spectral efficiency and mitigate interference in coordinated millimeter-wave systems, we proposes an optimal user group and beam selection scheme. The proposed scheme improves spectral efficiency by mitigating intra- and inter-cell interferences (ICI). By examining the effective channel capacity for all possible user combinations, user combinations and beams with minimized ICI can be selected. However, implementing this in a dense environment of cells and users requires highly complex computational abilities, which we have investigated applying multiclass classifiers based on machine learning. Compared with the conventional scheme, the numerical results show that our proposed scheme can achieve near-optimal performance, making it an attractive option for these systems.

• Korean Journal of Cognitive Science
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• v.27 no.2
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• pp.159-219
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• 2016

Mathematical ability is important for academic achievement and technological renovations in the STEM disciplines. This study concentrated on the relationship between neural basis of mathematical cognition and its mechanisms. These cognitive functions include domain specific abilities such as numerical skills and visuospatial abilities, as well as domain general abilities which include language, long term memory, and working memory capacity. Individuals can perform higher cognitive functions such as abstract thinking and reasoning based on these basic cognitive functions. The next topic covered in this study is about individual differences in mathematical abilities. Neural efficiency theory was incorporated in this study to view mathematical talent. According to the theory, a person with mathematical talent uses his or her brain more efficiently than the effortful endeavour of the average human being. Mathematically gifted students show different brain activities when compared to average students. Interhemispheric and intrahemispheric connectivities are enhanced in those students, particularly in the right brain along fronto-parietal longitudinal fasciculus. The third topic deals with growth and development in mathematical capacity. As individuals mature, practice mathematical skills, and gain knowledge, such changes are reflected in cortical activation, which include changes in the activation level, redistribution, and reorganization in the supporting cortex. Among these, reorganization can be related to neural plasticity. Neural plasticity was observed in professional mathematicians and children with mathematical learning disabilities. Last topic is about mathematical creativity viewed from Neural Darwinism. When the brain is faced with a novel problem, it needs to collect all of the necessary concepts(knowledge) from long term memory, make multitudes of connections, and test which ones have the highest probability in helping solve the unusual problem. Having followed the above brain modifying steps, once the brain finally finds the correct response to the novel problem, the final response comes as a form of inspiration. For a novice, the first step of acquisition of knowledge structure is the most important. However, as expertise increases, the latter two stages of making connections and selection become more important.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.1
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• pp.1-8
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• 2022

To understand physical phenomena of ship maneuvering deeply, a numerical study based on computational fluid dynamics is required. A computational method that can simulate the interaction between the ship hull, propeller, and rudder will provide informative local flows during ship maneuvering tests. The analysis of local flows can be applied to improve a physical model of ship maneuvering that has been widely used in maneuvering simulations. In this study, the numerical program named as WAVIS that has been developed for ship resistance and propulsion problems is extended to simulate ship maneuvering by free-running tests. The six degree-of-freedom of ship motion is implemented based on Euler angles and the overset technique is applied to treat the moving grid of ship hull and rudder. The propulsion force due to a propeller is calculated by a panel method that is based on the lifting-surface theory. The newly extended code is applied to simulate turning and zig-zag tests of KCS and the comparison with the available experimental data has been made.

• Structural Engineering and Mechanics
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• v.48 no.6
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• pp.849-878
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• 2013

This paper consists of two parts, which broadly examines solution techniques abilities for the structures with geometrical nonlinear behavior. In part I of the article, formulations of several well-known approaches will be presented. These solution strategies include different groups, such as: residual load minimization, normal plane, updated normal plane, cylindrical arc length, work control, residual displacement minimization, generalized displacement control, modified normal flow, and three-parameter ellipsoidal, hyperbolic, and polynomial schemes. For better understanding and easier application of the solution techniques, a consistent mathematical notation is employed in all formulations for correction and predictor steps. Moreover, other features of these approaches and their algorithms will be investigated. Common methods of determining the amount and sign of load factor increment in the predictor step and choosing the correct root in predictor and corrector step will be reviewed. The way that these features are determined is very important for tracing of the structural equilibrium path. In the second part of article, robustness and efficiency of the solution schemes will be comprehensively evaluated by performing numerical analyses.

• East Asian mathematical journal
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• v.30 no.2
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• pp.149-172
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• 2014

The problems of optimization addressed in the high school curriculum are usually posed in real-life contexts. However, because of the instructional purposes, problems are artificially constructed to suit computation, rather than to reflect real-life problems. Those problems have thus limited use for teaching 'practicalities', which is one of the goals of mathematics education. This study, by utilizing 'GeoGebra', suggests the optimization problem solving related to the quadratic curve, using the contour-line method which contemplates the quadratic curve changes successively. By considering more realistic situations to supplement the limit which deals only with numerical and algebraic approach, this attempt will help students to be aware of the usefulness of mathematics, and to develop interests in mathematics, as well as foster students' integrated thinking abilities across units. And this allows students to experience a variety of math.

• Tunnel and Underground Space
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• v.3 no.1
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• pp.63-79
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• 1993

The design of tunnels in rock masses often demands more informations on geologic features and rock mass properties than acquired by usual field survey and laboratory testings. In practice, the situation that a perfect set of geological and mechanical input data is given to geomechanics design engineer is rare, while the engineers are asked to achieve a high level of reliability in their design products. This study presents an artificial neural network which is developed to resolve the difficulties encountered in conventional design techniques, particulary the problem of deteriorating the confidence of existing numerical techniques such as the finite element, boundary element and distinct element methods due to the incomplete adn vague input data. The neural network has inferring capabilities to identify the possible failure modes, support requirements and its timing for underground openings, from previous case histories. Use of the neural network has resulted in a better estimate of the correlation between systems of rock mass classifications such as the RMR and Q systems. A back propagation learning algorithm together with a multi-layer network structure is adopted to enhance the inferential accuracy and efficiency of the neural network. A series of experiments comparing the results of the neural network with the actual field observations are performed to demonstrate the abilities of the artificial neural network as a new tunnel design assistance system.

• KIEE International Transactions on Power Engineering
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• v.4A no.1
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• pp.33-41
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• 2004

Knowledge of partial discharge (PD) is important to accurately diagnose and predict the condition of insulation. The PD phenomenon is highly complex and seems to be random in its occurrence. This paper indicates the possible use of chaos theory for the recognition and distinction concerning PD signals. Chaos refers to a state where the predictive abilities of a systems future are lost and the system is rendered aperiodic. The analysis of PD using deterministic chaos comprises of the study of the basic system dynamics of the PD phenomenon. This involves the construction of the PD attractor in state space. The simulation results show that the variance of an orthogonal axis in an attractor of chaos theory increases according to the magnitude and the number of PDs. However, it is difficult to clearly identify the characteristics of the PDs. Thus, we calculated the magnitude on an orthogonal axis in an attractor using singular value decomposition (SVD) and principal component analysis (PCA) to extract the numerical characteristics. In this paper, we proposed the condition monitoring method for gas insulated switchgear (GIS) using SVD for efficient calculation of the variance. Thousands of simulations have proven the accuracy and effectiveness of the proposed algorithm.

• Honam Mathematical Journal
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• v.36 no.4
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• pp.831-852
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• 2014

The obstacle shape reconstruction problem has been known to be difficult to solve since it is highly nonlinear and severely ill-posed. The use of local or locally supported basis functions for the problem has been addressed for many years. However, to the authors' knowledge, any research report on the proper usage of local or locally supported basis functions for the shape reconstruction has not been appeared in the literature due to many difficulties. The aim of this paper is to introduce the general concepts and methodologies for the proper choice and their implementation of locally supported basis functions through the two-dimensional Helmholtz equation. The implementations are based on the complex nonlinear parameter estimation (CNPE) formula and its robust algorithm developed recently by the authors. The basic concepts and ideas are simple. The derivation of the necessary properties needed for the shape reconstructions are elementary. However, the capturing abilities for the local geometry of the obstacle are superior to those by conventional methods, the trial and errors, due to the proper implementation and the CNPE algorithm. Several numerical experiments are performed to show the power of the proposed method. The fundamental ideas and methodologies described in this paper can be applied to many other shape reconstruction problems.

• Water Engineering Research
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• v.6 no.2
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• pp.73-89
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• 2005

Two kinds of high resolution GCMs with the same spatial resolutions but with different schemes run by domestic and foreign agencies are used to clarify the usefulness and sensitivity of GCM for water resources applications for Korea. One is AMIP-II (Atmospheric Model Intercomparison Project-II) type GCM simulation results done by ECMWF (European Centre for Medium-Range Weather Forecasts) and the other one is AMIP-I type GCM simulation results done by METRI (Korean Meteorological Research Institute). Observed mean areal precipitation, temperature, and discharge values on 7 major river basins were used for target variables. Monte Carlo simulation was used to establish the significance of the estimator values. Sensitivity analyses were done in accordance with the proposed ways. Through the various tests, discrimination condition is sensitive for the distribution of the data. Window size is sensitive for the data variation and the area of the basins. Discrimination abilities of each nodal value affects on the correct association. In addition to theses sensitivity analyses results, we also noticed some characteristics of each GCM. For Korean water resources, monthly and small window setting analyses are recommended using GCMs.