• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.38.2-38.2
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• 2016

The mapping of dark matter clustering from real to redshift spaces introduces the anisotropic property to the measured density power spectrum in redshift space, known as the Redshift Space Distortion (hereafter RSD) effect. The mapping formula is intrinsically non-linear, which is complicated by the higher order polynomials due to the indefinite cross correlations between the density and velocity fields, and the Finger-of-God (hereafter FoG) effect due to the randomness of the peculiar velocity field. Furthermore, the rigorous test of this mapping formula is contaminated by the unknown non-linearity of the density and velocity fields, including their auto- and cross-correlations, for calculating which our theoretical calculation breaks down beyond some scales. Whilst the full higher order polynomials remains unknown, the other systematics can be controlled consistently within the same order truncation in the expansion of the mapping formula, as shown in this paper. The systematic due to the unknown non-linear density and velocity fields is removed by separately measuring all terms in the expansion using simulations. The uncertainty caused by the velocity randomness is controlled by splitting the FoG term into two pieces, 1) the non-local FoG term being independent of the separation vector between two different points, and 2) the local FoG term appearing as an indefinite polynomials which is expanded in the same order as all other perturbative polynomials. Using 100 realizations of simulations, we find that the best fitted non-local FoG function is Gaussian, with only one scale-independent free parameter, and that our new mapping formulation accurately reproduces the observed power spectrum in redshift space at the smallest scales by far, up to k ~ 0.3 h/Mpc, considering the resolution of future experiments.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.768-773
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• 2011

The prediction method of future events using the time histories of velocity or pressure, etc., is a useful way for controlling various air vehicles. For example, the sensors of velocity or pressure can be used to extract the time mode coefficients of eigenmode of flow field, and then the result is applied to suppress wake or drag. The velocity information is mapped to the entire flow field, so this mapping function can be used to predict the future events based on the current information. The mapping function is composed of the huge amount of weight parameters, so the efficient way of finding these parameters is needed. Here, the neural network algorithm is studied to draw a mapping function using the number and location of velocity sensors.

• Wind and Structures
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• v.27 no.5
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• pp.347-356
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• 2018

In this paper, stress distribution for a structurally stable greenhouse is considered in the present paper with subsequent investigation into the detailed stress distribution contour with the variation of self-weight and wind pressure level designation method under wind velocity of less than 30 m/sec. For reliable analysis, wind pressure coefficients of a single greenhouse unit were modeled and compared with experiment with correlation coefficient greater than 0.99. Wind load level was designated twofold: direct mapping of fluid dynamic analysis and conversion of modeled results into wind pressure coefficients ($C_P$). Finally, design criteria of EN1991-1-4 and NEN3859 were applied in terms of their wind pressure coefficients for comparison. $C_P$ of CFD result was low in the most of the modeled area but was high only in the first roof wind facing and the last lee facing areas. Besides, structural analysis results were similar in terms of stress distribution as per EN and direct mapping while NEN revealed higher level of stress for the last roof area. The maximum stress levels are arranged in decreasing order of mapping, EN, and NEN, generating 8% error observed between the EN and mapping results under 30 m/sec of wind velocity. On the other hand, effect of dead weight on the stress distribution was investigated via variation of high stress position with wind velocity, confirming shift of such position from the center to the forward head wind direction. The sensitivity of stress for wind velocity was less than 0.8% and negligible at wind velocity greater than 20 m/sec, thus eliminating self-weight effect.

• Journal of The Geomorphological Association of Korea
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• v.23 no.1
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• pp.103-115
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• 2016

At present, an acoustic Doppler current profiler (ADCP) is one of the most suitable tools for measurement of three dimensional flow characteristics in the river. The data resulting from this approach can be used for flow visualization and velocity mapping together with post-processing software tools. Among them, 'adcptools' is the latest one and provides more realistic velocity distribution in the cross-section since it uses velocity along the beam direction. In this study, a flow analysis was made using the 'adcptools' for the Amazon River and the Han River dataset. Discharge was recalculated and accuracy of discharge and velocity was evaluated. Streamwise velocity distribution and secondary flow pattern in cross-sections were visualized. Geo-referenced velocity distribution was also mapped. A summary with future prospect of 'adcptools' for studies on fluvial geomorphology is briefly given.

• The Journal of Korea Robotics Society
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• v.10 no.4
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• pp.230-244
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• 2015

Continuous-path motion control such as resolved motion rate control requires online solving of the inverse differential kinematics for a robot. However, the solution space of the inverse differential kinematics related to Jacobian J is not well-established. In this paper, the solution space of inverse differential kinematics is analyzed through categorization of mapping conditions between joint velocities and end-effector velocity of a robot. If end-effector velocity is within the column space of J, the solution or the minimum norm solution is obtained. If it is not within the column space of J, an approximate solution by least-squares is obtained. Moreover, this paper introduces an improved mapping diagram showing orthogonality and mapping clearly between subspaces, and concrete examples numerically showing the concept of several subspaces. Finally, a solver and graphics user interface (GUI) for inverse differential kinematics are developed using MATLAB, and the solution of inverse differential kinematics using the GUI is demonstrated for a vertically articulated robot.

• Journal of Astronomy and Space Sciences
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• v.27 no.1
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• pp.21-30
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• 2010

In processing space geodetic data, mapping functions are used to convert the tropospheric signal delay along the zenith direction to the line of sight direction. In this study, we compared three mapping functions by evaluating their effects on the tropospheric signal delay and position estimates in GPS data processing. The three mapping functions tested are Niell Mapping Function (NMF), Vienna Mapping Function 1 (VMF1), and Global Mapping Function (GMF). The tropospheric delay and height estimates from VMF1 and GMF are compared with the ones obtained with NMF. The differences among mapping functions show annual signals with the maximum occurring in February or August. To quantitatively estimate the discrepancies among mapping functions, we calculated the maximum difference and the amplitude using a curve fitting technique. Both the maximum difference and amplitude have high correlations with the latitude of the site. Also, the smallest difference was found around $30^{\circ}N$ and the amplitudes increase toward higher latitudes. In the height estimates, the choice of mapping function did not significantly affect the vertical velocity estimate, and the precision of height estimates was improved at most of the sites when VMF1 or GMF was used instead of NMF.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.2
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• pp.123-128
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• 2022

This paper proposes a mobile robot platform for education that can experiment with various autonomous driving algorithms such as obstacle avoidance and path planning. The platform consists of a robot module and a remote controller module, both of which are based on the Arduino Nano 33 IoT embedded board. The robot module is designed as a differential drive type using two encoder motors, and the speed of the motor is controlled using PID control. In the case of the remote controller module, a command to control the robot platform is received with a 2-axis joystick input, and an elliptical grid mapping technique is used to convert the joystick input into a linear and angular velocity command of the robot. WiFi and Zigbee are used for communication between the robot module and the remote controller module. The proposed robot platform was tested by measuring and comparing the linear velocity and angular velocity of the actual robot according to the linear velocity and angular velocity commands of the robot generated by the input of the joystick.

• Wind and Structures
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• v.38 no.4
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• pp.231-244
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• 2024

As wind farms expand into low wind speed areas, an increasing number are being established in mountainous regions. To fully utilize wind energy resources, it is essential to understand the details of mountain flow fields. Reconstructing the wind speed field in complex terrain is crucial for planning, designing, operation of wind farms, which impacts the wind farm's profits throughout its life cycle. Currently, wind speed reconstruction is primarily achieved through physical and machine learning methods. However, physical methods often require significant computational costs. Therefore, we propose a Full Convolutional Neural Network (FCNN)-based reconstruction method for mountain wind velocity fields to evaluate wind resources more accurately and efficiently. This method establishes the mapping relation between terrain, wind angle, height, and corresponding velocity fields of three velocity components within a specific terrain range. Guided by this mapping relation, wind velocity fields of three components at different terrains, wind angles, and heights can be generated. The effectiveness of this method was demonstrated by reconstructing the wind speed field of complex terrain in Beijing.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.304-307
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• 2008

Two-dimensional Stokes flow due to the line source and line sink of same strength in semi-infinite flow region with free surface is analysed using complex variable theory and conformal mapping. Surface tension effects are included while gravity is neglected. From the results of analysis, flow pattern and free surface shape are obtained and velocity distribution on the free surface is determined with 2 independent parameters Ca (capillary number) and h (non-dimensionalized distance between source and sink). When the location of the sink is above the source, velocity on the free surface converges and a cusp occurs on the free surface for the value of Ca above some critical capillary number.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.679-684
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• 2000

This paper presents the development of system and program for a Parallel-Typed CNC Machine. The system consists of parallel manipulator, PC (Personal Computer), DMC (DSP Motion Controller), and machining tools. In order to control the manipulator, the program, which is implemented in "c/c++" language, involves inverse/direct kinematics, velocity mapping, Jacobian and etc. A controller computes the kinematic formulation in real-time and generates and motion by the DMC. A monitor, which has access to program and sensory information, displays the status of manipulator.nipulator.