• Steel and Composite Structures
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• v.19 no.3
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• pp.697-711
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• 2015

It is well known the importance of considering hysteretic energy demands for the seismic assessment and design of structures. In such a way that it is necessary to establish new parameters of the earthquake ground motion potential able to predict energy demands in structures. In this paper, several alternative vector-valued ground motion intensity measures (IMs) are used to estimate hysteretic energy demands in steel framed buildings under long duration narrow-band ground motions. The vectors are based on the spectral acceleration at first mode of the structure Sa($T_1$) as first component. As the second component, IMs related to peak, integral and spectral shape parameters are selected. The aim of the study is to provide new parameters or vector-valued ground motion intensities with the capacity of predicting energy demands in structures. It is concluded that spectral-shape-based vector-valued IMs have the best relation with hysteretic energy demands in steel frames subjected to narrow-band earthquake ground motions.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.6
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• pp.393-400
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• 2022

The Divert and Attitude Control System(DACS) used in high-altitude engagements is expensive and complex. In this paper, we design a high-altitude terminal guidance and control loop of guided-missile equipped with a Thrust Vector Control(TVC) that is less expensive and simpler than DACS. The proposed system utilizes a quaternion feedback control technique to track the thrust attitude command converted from the acceleration command of true proportional navigation guidance. The performance analysis of the proposed terminal guidance and control loop is conducted through engagement simulations against ballistic targets at a high altitude.

• Korean Journal of Applied Biomechanics
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• v.20 no.2
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• pp.191-196
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• 2010

The purpose of this study was to investigate the effect of gait speed and walkway slope on the body acceleration, for the future validation of using an accelerometer in the estimation of energy consumption. Ten young healthy subjects with accelerometers on the upper thigh and ankle walked on a treadmill at 9 conditions(three speeds ${\times}$ three slopes) for 5 minutes. Acceleration signals of four directions, i.e. anterior-posterior(AP), medio-lateral(ML), superior-inferior(SI) and vector sum(VS) directions, of each sensor were measured, and root means squared(RMS) values of them were used as analysis variables. As statistical analysis, repeated measure two-way ANOVA was performed for RMS accelerations at each attachment sites, with slope and velocity as independent factors. At both the upper thigh and ankle, RMS acceleration of all directions were affected by gait velocities(p<.001) showing greater accelerations for higher velocities. Contrary to expectations, no slope effect existed in RMS accelerations at hip. Moreover, RMS acceleraion at ankle decreased with slope in SI and VS directions(p<.01). These results suggests that RMS acceleration cannot reflect the change in physical activity due to the change in walkway slope.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.552-554
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• 1998

In target tracking problems the fixed gain Kalman filter is primarily used to predict a target state vector. This filter, however, has a poor precision for maneuvering targets while it has a good performance for non-maneuvering targets. To overcome the problem this paper proposes the system which estimates the acceleration with neural networks using the input estimation technique. The ability to efficiently fuse information of different forms is one of the major capabilities of trained multi-layer neural networks. The primary motivation for employing neural networks in these applications comes from the efficiency with which more features can be utilized as inputs for estimating target maneuvers. The parallel processing capability of a properly trained neural network can permit fast processing of features to yield correct acceleration estimates. The features used as inputs can be extracted from the combinations of innovation data and heading changes, and for this we set the two dimensional model. The properly trained neural network system outputs the acceleration estimates and compensates for the primary Kalman filter. Finally the proposed system shows the optimum performance.

• Smart Structures and Systems
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• v.17 no.6
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• pp.903-915
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• 2016

The classical Kalman filter (KF) provides a practical and efficient state estimation approach for structural identification and vibration control. However, the classical KF approach is applicable only when external inputs are assumed known. Over the years, some approaches based on Kalman filter with unknown inputs (KF-UI) have been presented. However, these approaches based solely on acceleration measurements are inherently unstable which leads poor tracking and so-called drifts in the estimated unknown inputs and structural displacement in the presence of measurement noises. Either on-line regularization schemes or post signal processing is required to treat the drifts in the identification results, which prohibits the real-time identification of joint structural state and unknown inputs. In this paper, it is aimed to extend the classical KF approach to circumvent the above limitation for real time joint estimation of structural states and the unknown inputs. Based on the scheme of the classical KF, analytical recursive solutions of an improved Kalman filter with unknown excitations (KF-UI) are derived and presented. Moreover, data fusion of partially measured displacement and acceleration responses is used to prevent in real time the so-called drifts in the estimated structural state vector and unknown external inputs. The effectiveness and performance of the proposed approach are demonstrated by some numerical examples.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.1
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• pp.15-28
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• 2018

Recently Machine Learning algorithms are widely used to process Big Data in various applications and a lot of these applications are executed in run time. Therefore the speed of Machine Learning algorithms is a critical issue in these applications. However the most of modern iteration Machine Learning algorithms use a successive iteration technique well-known in Numerical Linear Algebra. But this technique has a very low convergence, needs a lot of iterations to get solution of considering problems and therefore a lot of time for processing even on modern multi-core computers and clusters. Tchebychev iteration technique is well-known in Numerical Linear Algebra as an attractive candidate to decrease the number of iterations in Machine Learning iteration algorithms and also to decrease the running time of these algorithms those is very important especially in run time applications. In this paper we consider the usage of Tchebychev iterations for acceleration of well-known K-Means and SVM (Support Vector Machine) clustering algorithms in Machine Leaning. Some examples of usage of our approach on modern multi-core computers under Apache Spark framework will be considered and discussed.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.543-546
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• 2007

Flow visualization is one of visualization techniques and it means a visual expression of vector data using 2D or 3D graphics. It aims for human to easily find and understand a special feature of the vector data. The Image Based Flow Visualization (IBFV) is one of the fastest technique in the dense integration based flow visualization techniques. In this paper, IBFV is accelerated and implemented using commodity GPU. Especially, mesh advection is accelerated at the vertex program.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.91.3-91
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• 2002

This paper describes a design of an induction motor control system for industrial sewing machines. On the basis of vector control principle, the control system is simulated by using the ACSL, implemented on a DSP(TMS320C31).A space vector modulation is used as the inverter switching strategy. For the application of industrial sewing machines, A fast acceleration (deceleration) and removal of velocity ripples are required, because a sewing quality and sewing machines life time depends on these characteristics. The designed control system has fast dynamic characteristics and small speed vibration. The result is applied to the industrial sewing machine and result are shown.

• Journal of Mechanical Science and Technology
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• v.19 no.2
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• pp.529-539
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• 2005

The development of a fast, accurate, and inexpensive position-controlled pneumatic actuator that may be applied to various practical positioning applications with various external loads is described in this paper. A novel modified pulse-width modulation (MPWM) valve pulsing algorithm allows on/off solenoid valves to be used in place of costly servo valves. A comparison between the system response of the standard PWM technique and that of the modified PWM technique shows that the performance of the proposed technique was significantly increased. A state-feedback controller with position, velocity and acceleration feedback was successfully implemented as a continuous controller. A switching algorithm for control parameters using a learning vector quantization neural network (LVQNN) has newly proposed, which classifies the external load of the pneumatic actuator. The effectiveness of this proposed control algorithm with smooth switching control has been demonstrated through experiments with various external loads.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1437-1445
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• 2014

A smartphone has very limited input methods regardless of its various functions. In this respect, it is one alternative that sensor motion recognition can make intuitive and various user interface. In this paper, we recognize user's motion using acceleration sensor, magnetic field sensor, and gyro sensor in smartphone. We try to reduce sensing error by gradient descent algorithm because in single sensor it is hard to obtain correct data. And we apply vector quantization by conversion of rotation displacement to spherical coordinate system for elevated recognition rate and recognition of small motion. After vector quantization process, we recognize motion using HMM(Hidden Markov Model).