• Wind and Structures
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• v.21 no.5
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• pp.461-487
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• 2015

In recent years, the Graphics Processing Unit (GPU) has become a competitive computing technology in comparison with the standard Central Processing Unit (CPU) technology due to reduced unit cost, energy and computing time. This paper describes the derivation and implementation of GPU-based algorithms for the analysis of wind loading uncertainty on high-rise systems, in line with the research field of probability-based wind engineering. The study begins by presenting an application of the GPU technology to basic linear algebra problems to demonstrate advantages and limitations. Subsequently, Monte-Carlo integration and synthetic generation of wind turbulence are examined. Finally, the GPU architecture is used for the dynamic analysis of three high-rise structural systems under uncertain wind loads. In the first example the fragility analysis of a single degree-of-freedom structure is illustrated. Since fragility analysis employs sampling-based Monte Carlo simulation, it is feasible to distribute the evaluation of different random parameters among different GPU threads and to compute the results in parallel. In the second case the fragility analysis is carried out on a continuum structure, i.e., a tall building, in which double integration is required to evaluate the generalized turbulent wind load and the dynamic response in the frequency domain. The third example examines the computation of the generalized coupled wind load and response on a tall building in both along-wind and cross-wind directions. It is concluded that the GPU can perform computational tasks on average 10 times faster than the CPU.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.4
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• pp.458-464
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• 2014

This paper proposes the rotor position and rotor speed estimation for an interior permanent magnet synchronous machines (IPMSM) using Unscented Kalman Filter (UKF) in alpha-beta coordinate system. Conventional algorithms using UKF are based on the simple observer model of IPMSM in d-q coordinate system. Rotor acceleration is neglected within the sampling step. An expansion of the observer model in an alpha-beta coordinate system with the consideration of the rotor speed variation provides the improved rotor position and speed estimation. The results show good stability concerning the expansion of observer model for the IPMSM.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.844-849
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• 1991

A high speed control system for a telerobot using DSP is developed. The system is designed to resolve computational burden in advanced algorithms. The design is assumed to h ave no specific algorithm and robot configuration. The system is composed of a teaching box, a DSP board, a set of servo drivers and 16 bit microcomputer system. The teaching box is designed as a man-machine interface, which has two joysticks with three degrees of freedom for velocity generation in Cartesian space. The DSP board, i.e. DSP56000ADS based on a 10.25MIPS digital signal processor, DSP56001, computes the inverse Jacobian matrix which transforms Cartesian velocity into joint velocity. A resolved motion rate control algorithm for a 5 degrees of freedom manipulator was implemented. About 100 Hz sampling rate was achieved in this system.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.979-982
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• 2001

In CNC machining, demands on precision machining of free formed surface model are increasing. Most of the CAD/CAM systems provide the NURBS(Non-Uniform Rational B-Spline) interpolator. NURBS is defined with NURBS parameter by control point, weight value and knot value. This paper shows the realtime NURBS interpolation algorithms and compared with each other. One is based on the equal length of curve segments rather than equal increment of the parameter Δu. The other is to limit the interpolation error to any desired level by adjusting the feedrate considering the curvature of the shape and sampling time.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.119-122
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• 1995

Neural network has found wide applications in the system identification, modeling, and realization based on its function approximation capability. THe system governe dby nonlinear dynamics is hard to be identified by the neural network because there exist following difficulties. FIrst, the training samples obtained by the stae trajectory are apt to be nonuniform over the region of interest. Second, the system may becomje unstable while attempting to obtain the samples. This paper deals with these problems in discrete-time system and suggest effective solutions which provide stability and uniform sampliing by the virtue of robust control theory and heuristic algorithms.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.459-464
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• 2001

For major nuclear power plant components periodic inspections and integrity assessments are needed for the safety. But many flaws are undetectable due to sampling inspection. Probabilistic integrity assessment is applied to take into consideration of uncertainty and variance of input parameters arise due to material properties, applied load and undetectable flaws. This paper describes a Probabilistic Fracture Mechanics(PFM) analysis based on Monte Carlo(MC) algorithms. Taking important parameters as probabilistic variables such as fracture toughness, crack growth rate and flaw shape, failure probability of major nuclear power plant components is archived as a results of MC simulation. For the verification of these analysis, a comparison study of the PFM analysis using other commercial code, mathematical method is carried out and a good agreement was observed between those results.

• Journal of Power Electronics
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• v.12 no.3
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• pp.509-517
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• 2012

This paper presents control strategies for cascaded H-bridge multilevel active power filters (APFs). A current loop controller is implemented using a proportional-resonant (PR) regulator, which achieves zero steady-state error at target frequencies. The power balancing mechanism for the dc-link capacitor voltages is analyzed and a voltage balancing controller is presented. To mitigate the picket-fence effect of the conventional FFT algorithm under asynchronous sampling conditions, the Hanning Windowed-FFT algorithm is proposed for reference current generation (RCG). This calculates the frequency, amplitude and phase of individual harmonic components accurately and as a result, selective harmonic compensation (SHC) is achieved. Simulation and experimental results are presented, which verify the validity and effectiveness of the devised control algorithms.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.114-122
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• 2021

In this paper, an automated vehicle intersection collision accident was analyzed through simulation. Recently, the more automated vehicles are distributed, the more accidents related to automated vehicles occur. Accidents may show different trends depending on the sensor characteristics of the automated vehicle and the performance of the accident prevention system. Based on NASS-CDS (National Automotive Sampling System-Crashworthiness Data System) and TAAS (Traffic Accident Analysis System), four scenarios are derived and simulations are performed. Automated vehicles are applied with a virtual system consisting of an autonomous emergency braking system and algorithms that predict the route and avoid collisions. The simulations are conducted by changing the sensor angle, vehicle speed, the range of the sensor and vehicle speed range. A range of variables considered vehicle collision were derived from the simulation.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.294-297
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• 2024

Malware detection has become increasingly critical with the proliferation of end devices. To improve detection rates and efficiency, the research focus in malware detection has shifted towards leveraging machine learning and deep learning approaches. This shift is particularly relevant in the context of the widespread adoption of end devices, including smartphones, Internet of Things devices, and personal computers. Machine learning techniques are employed to train models on extensive datasets and evaluate various features, while deep learning algorithms have been extensively utilized to achieve these objectives. In this research, we introduce TabNet, a novel architecture designed for deep learning with tabular data, specifically tailored for enhancing malware detection techniques. Furthermore, the Synthetic Minority Over-Sampling Technique is utilized in this work to counteract the challenges posed by imbalanced datasets in machine learning. SMOTE efficiently balances class distributions, thereby improving model performance and classification accuracy. Our study demonstrates that SMOTE can effectively neutralize class imbalance bias, resulting in more dependable and precise machine learning models.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.337-345
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• 2023

In this paper, we propose a 3D LiDAR sensor-based costmap generation and path planning algorithm using it for reliable autonomous flight in complex indoor environments. 3D path planning is essential for reliable operation of UAVs. However, existing grid search-based or random sampling-based path planning algorithms in 3D space require a large amount of computation, and UAVs with weight constraints require reliable path planning results in real time. To solve this problem, we propose a method that divides a 3D space into several 2D spaces and a path planning algorithm that considers the distance to obstacles within each space. Among the paths generated in each space, the final path (Best path) that the UAV will follow is determined through the proposed objective function, and for this purpose, we consider the rotation angle of the 2D space, the path length, and the previous best path information. The proposed methods have been verified through autonomous flight of UAVs in real environments, and shows reliable obstacle avoidance performance in various complex environments.