• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1901-1906
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• 2010

This classification of walking patterns is important and many kinds of applications. Therefore, we attempted to classify walking on level ground from slow walking to fast walking using a waist acceleration signal. A tri-axial accelerometer was fixed to the subject's waist and the three acceleration signals were recorded by bluetooth module at a sampling rate of 100 Hz eleven healthy. The data were analyzed using discrete wavelet transform. Walking patterns were classified using two parameters; One was the ratio between the power of wavelet coefficients which were corresponded to locomotion and total power in the anteroposterior direction (RPA). The other was the ratio between root mean square of wavelet coefficients at the anteroposterior direction and that at the vertical direction(RAV). Slow walking could be distinguished by the smallest value in RPA from other walking pattern. Fast walking could be discriminated from level walking using RAV. It was possible to classify the walking pattern using acceleration signal in healthy people.

• International Journal of Highway Engineering
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• v.9 no.4
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• pp.215-226
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• 2007

To verify the performance of roadside barriers, occupant risk indices are calculated from acceleration and angular velocity data of vehicle crash tests. The occupant risk indices to be computed include THIV(Theoretical Head Impact Velocity), PHD(Post-impact Head Deceleration), ASI(Acceleration Severity Index), OIV(Occupant Impact Velocity) and ORA(Occupant Ridedown Acceleration). There is a confusion due to different values of occupant risk indices produced for the same test data because various computational procedures and data processing methods can be applied to compute them. To slove this problem the effects of various numerical procedures and data processing methods on occupant risk indices were investigated. If the sampling rate specified in the guidelines is used for full-scale vehicle crash tests, an interpolation of impact time and numerical integration methods do not result in an appreciable change of THIV and OIV. The way to determine 10msec moving average for PHD and zero offset of data processing should be specified in the guidelines because 10msec moving average and zero offset methods have a significant influence on occupant risk indices.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.4
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• pp.412-418
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• 2014

In this paper, for better boarding performance and pleasant boarding sensitivity of the ship, comparison and analysis was performed of motion sickness questionnaire with MSI(Motion Sickness Incidence) calculation based on ship motion theory(Strip Method) due to sea condition, incident angle in main sail way, economic speed, and calculation position of the training ship Kaya of Pukyong National University. On theses works, the rougher sea conditions became, the higher total motion sickness rate was occurred. The weights of vertical acceleration and the rates of MSI were higher at the bridge and the accommodation, which were located farther from the center of gravity of the ship. And effects of the vertical acceleration of the ship were increased in rolling then in head sea. In comparison between motion sickness questionnaire with MSI calculation, when the vertical acceleration increased, the motion sickness rate increased. The location to increase vertical acceleration and the location to cause motion sickness were agreed.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.51 no.1
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• pp.71-78
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• 2015

In order to deduce an objective evaluation method of motion seasickness incidence (MSI) by ship motions during underway in irregular waves and to present the fundamental data of passenger comfort on the yacht and the passenger ship according to the result, the MSI of the trainees by the questionnaires was analysed and compared with the rate of variation of salivary ${\alpha}$-amylase activity (VSAA) on the training ship "A-ra ho" of Jeju national university. Relationship between rate of variation (x) by salivary ${\alpha}$-amylase activity and motion seasickness incidence (y) was described by the equation, MSI(%) = 0.6073 x + 12.189 including the correlation coefficient ($R^2=0.9853$). The result obtained through the rate of variation of salivary ${\alpha}$-amylase activity which was the quantitative evaluation method for ship motions causing seasickness was most affected by z-vertical acceleration and occurred within the frequency range 0.1 to 0.3Hz centered on 0.2Hz, and the simulation result based on this finding showed the motion seasickness rate at approximately 4% lower than the rate obtained through the survey.

• Journal of the Korean Mathematical Society
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• v.53 no.3
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• pp.691-707
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• 2016

In this paper, we first introduce two one-parameter relaxation (OPR) iterative methods for solving singular saddle point problems whose semi-convergence rate can be accelerated by using scaled preconditioners. Next we present formulas for finding their optimal parameters which yield the best semi-convergence rate. Lastly, numerical experiments are provided to examine the efficiency of the OPR methods with scaled preconditioners by comparing their performance with the parameterized Uzawa method with optimal parameters.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.377.2-377
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• 2002

In optical disk drives (ODD), the demands of high data density and high speed have been increasing rapidly to achieve high data capacity and data transfer rate The use of short wavelength laser and high track following performance are needed to raise data density and data rate. For high-performance actuator, the improvement of linearity and acceleration become more important. (omitted)

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.33-38
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• 1989

This paper is about construction of Data Acquisition System for Gyro-System which obtains the information such as position, velocity, acceleration and attitude etc of Dynamic Vehicle System. We made up the Data Acquisition System using IBM-PC and in connection with CYBER 180-830 based on rate table, controller and IEEE 488 Interface etc.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.881-882
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• 2011

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.11
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• pp.1012-1019
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• 2015

This paper shows the acceleration of iterative physical optics(IPO) for radar cross section(RCS) by using two techniques effectively. For the analysis of the multiple reflection in the cavity, IPO uses the near field method, unlike shooting and bouncing rays method which uses the geometric optics(GO). However, it is still far slower than physical optics(PO) and it is needed to accelerate the speed of IPO for practical purpose. In order to address this problem, graphic processing unit(GPU) can be applied to reduce calculation time and adaptive iterative physical optics-change rate(AIPO-CR) method is also applicable effectively to optimize iteration for acceleration of calculation.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.17-25
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• 2023

As the range of use of urban unmanned aerial vehicles (UAV) expands, it is necessary to operate UAVs efficiently because of its limited battery capacity. For this, it is required to find the optimal flight profile with various simulations. Therefore, it is important to predict the power and energy consumption of the UAV battery. In this paper, we analyzed the relationship between the speed and acceleration of the UAV and power consumption during the flight. Then, we derived a linear model, which is easily utilized. In addition, we also derived an accurate power consumption model based on deep neural network learning. To find the efficient model, we used learning data as 1) the GPS 3-axis velocity and acceleration data, 2) the IMU 3-axis velocity only, and 3) the IMU 3-axis velocity and acceleration data. The final model shows 5.86% error rate for power consumption and 1.50% error rate for the cumulative energy consumption.