• Journal of IKEEE
• /
• v.24 no.4
• /
• pp.1058-1063
• /
• 2020

This paper proposes a 12-bit SAR A/D(Successive Approximation Register Analog-to-Digital) converter that operates at low power for bio-signal and sensor signal processing. The conventional SAR A/D converter utilized the reduction of the dynamic current, which resulted in reducing total power consumption. In order to solve the limitation of the sampling time due to charging/discharging of the capacitor for reducing dynamic current, the different sampling time on the C-DAC bit switch operation was applied to reduce the dynamic current. In addition, lowering the supply voltage of the digital block to 0.6V led to 70% reduction of the total power consumption of the proposed ADC. The proposed SAR A/D was implemented with CMOS 65nm process 1-poly 6-metal, operates with a supply voltage of 1.2V. The simulation results demonstrate that ENOB, DNL/INL, power consumption and FoM are 10.4 bits, ±0.5LSB./±1.2LSB, 31.2uW and 2.8fJ/step, respectively.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.14 no.6
• /
• pp.706-711
• /
• 2014

A low power 6-bit flash ADC that uses an input voltage range detection algorithm is described. An input voltage level detector circuit has been designed to overcome the disadvantages of the flash ADC which consume most of the dynamic power dissipation due to comparators array. In this work, four digital input voltage range detectors are employed and each input voltage range detector generates the specific clock signal only if the input voltage falls between two adjacent reference voltages applied to the detector. The specific clock signal generated by the detector is applied to turn the corresponding latched comparators on and the rest of the comparators off. This ADC consumes 68.82 mW with a single power supply of 1.2V and achieves 4.3 effective number of bits for input frequency up to 1 MHz at 500 MS/s. Therefore it results in 4.6 pJ/step of Figure of Merit (FoM). The chip is fabricated in 0.13-um CMOS process.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.6
• /
• pp.2106-2117
• /
• 2017

An effective statistical feature extraction approach of data sampling of fault in the combined transmission system is presented in this paper. The proposed algorithm leads to high accuracy at minimum cost to predict fault location and fault type classification. This algorithm requires impedance measurement data from one end of the transmission line. Modal decomposition is used to extract positive sequence impedance. Then, the fault signal is decomposed by using discrete wavelet transform. Statistical sampling is used to extract appropriate fault features as benchmark of decomposed signal to train classifier. Support Vector Machine (SVM) is used to illustrate the performance of statistical sampling performance. The overall time of sampling is not exceeding 1 1/4 cycles, taking into account the interval time. The proposed method takes two steps of sampling. The first step takes 3/4 cycle of during-fault and the second step takes 1/4 cycle of post fault impedance. The interval time between the two steps is assumed to be 1/4 cycle. Extensive studies using MATLAB software show accurate fault location estimation and fault type classification of the proposed method. The classifier result is presented and compared with well-established travelling wave methods and the performance of the algorithms are analyzed and discussed.

• Journal of Korea Multimedia Society
• /
• v.19 no.6
• /
• pp.1034-1042
• /
• 2016

The detection of P-waves and T-wave in the electrocardiogram signal analysis is an important issue. But the accuracy of the boundary detection algorithm is an insufficient level in the change of slow transition in the signal compared to the QRS complex. This study proposes an algorithm to detect P-wave and T-wave sequentially after determining local baseline using QRS complex. First, we detected the peak points based on local baseline and determined the onset and offset through the calculation of the area of the section. After modifying the baseline using detected waveform, we detected the other waveform in the same way and separated the P-wave and the T-wave based on the location. We used the PhysioNet QT database to evaluate the performances of the algorithm, and calculate the mean and the standard deviations. The experiment results show that standard deviations are under the tolerances accepted by expert physicians, and outperform the results obtained by the other algorithms.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.25 no.6
• /
• pp.442-450
• /
• 2020

A small-signal modeling approach for a three-level boost (TLB) converter and a design methodology for a double-loop controller are proposed in this study. Conventional modeling of TLB converters involves three state variables. Moreover, TLB converters have two operation modes depending on the duty ratio. Consequently, complex mathematical calculations are required for controller design. This study proposes a simple system modeling method that uses two state variables, unlike previous methods that require three state variables. Analysis shows that the transfer functions of the two operation modes can be expressed as identical equations. This condition means that the linear feedback controller can be applied to all operational ranges, that is, for full duty ratios. The design method for a double-loop controller using a PI controller is presented in step-by-step sequences. Simulation and experimental verifications are conducted to verify the effectiveness of the small-signal analysis and control system design.

• Journal of Sensor Science and Technology
• /
• v.31 no.6
• /
• pp.411-417
• /
• 2022

In this paper, we propose a sensor with a C-shaped load cell to detect force change when a person sitting on the chair in an electrical transport-convenience vehicle is pushing ground by both heels. The load cell built in the vehicle is mechanically deformed by the vertical force owing to the human weight and the horizontal force by ground-pushing feet. The deformation rate of the load cell and its distribution are simulated using finite element analysis. In the simulation, the applied loads are preset in the range of 10 kg - 100 kg with a step size of 10 kg, and the ground-pushing force by feet is increased to 40 N with a step size of 5 N with respect to each applied load level. The resistance change of the load cell was observed to be linear in simulation as well as in measurement. the maximum difference between simulation and measurement was 0.89 % when the strain gauge constant was 2.243. The constant has a large influence on the difference. The proposed sensor was fabricated by connecting an instrument amplifier and a microcontroller to a load cell and used to detect the force by ground-pushing feet. To detect foot driving, the reference signal was set to 130% of the load, and the duration of the sensor output signal exceeding the reference signal was set to 0.6 s. In a test of a vehicle built with the proposed sensor, the footpushing force by the worker could be successfully detected even when the worker was working.

• Journal of Korea Multimedia Society
• /
• v.21 no.11
• /
• pp.1244-1253
• /
• 2018

The color of traffic light is sensitive to various illumination conditions. Especially it loses the hue information when oversaturation happens on the lighting area. This paper proposes a traffic light recognition method robust to these illumination variations. The method consists of two steps of traffic light detection and recognition. It just uses the intensity and saturation in the first step of traffic light detection. It delays the use of hue information until it reaches to the second step of recognizing the signal of traffic light. We utilized a deep learning technique in the second step. We designed a deep convolutional neural network(DCNN) which is composed of three convolutional networks and two fully connected networks. 12 video clips were used to evaluate the performance of the proposed method. Experimental results show the performance of traffic light detection reporting the precision of 93.9%, the recall of 91.6%, and the recognition accuracy of 89.4%. Considering that the maximum distance between the camera and traffic lights is 70m, the results shows that the proposed method is effective.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2005.10a
• /
• pp.744-747
• /
• 2005

This research studied 6-Axes Articulated Robot Manipulator's gain Tuning in consideration of dynamic. First of all, search fur proportional gain of velocity control loop by dynamic signal analyzer. Proportional gain of velocity control loop is connected to dynamic signal analyzer. Next Select free Proportional Gain value. And Select amplitude X of sinusoidal properly so that enough Velocity Feedback Signal may be paid as there is no group to utensil department. Next step, We can get Bode Diagram of Closed loop transfer function response examination in interested frequency. Integral calculus for gain of velocity loop is depended on integral calculus correction's number. We can obtain open loop transfer function by integrator. And we can know bode diagram's special quality from calculated open loop transfer function. With this, Velocity Control Loop's Parameter as inner loop is controlled. Next In moving, when vibration occurs, it controls notch filter. And finally, we have to control fred-forward filter parameter for elevation of control performance.

• Korean Journal of Optics and Photonics
• /
• v.23 no.6
• /
• pp.246-250
• /
• 2012

We derive analytical expressions for the output spectral density and the noise power $P_{\beta}$ in noise loading analysis using Volterra kernels to characterize fiber nonlinearities. The bandwidth of the input noise source has little effect on $P_{\beta}$, but the power of the input noise source and the dispersion parameter value of the fiber have a significant effect on $P_{\beta}$. The Volterra method predicts ${\Delta}P_{\beta}[dB]$ = 30 dB/decade, which agrees very accurately over a wide range of fiber parameters compared with the numerical results by the split-step Fourier method. Therefore the Volterra method could be useful to predict the performance of a dense WDM system when we plan to upgrade fiber or increase signal power.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.36 no.6
• /
• pp.490-495
• /
• 2016

Pulsed eddy current (PEC) testing signals have typically been obtained from the electromotive force induced in a sensor coil. However, an increasing number of studies have elected to incorporate the Hall plate as a sensor. Thus, accurate numerical modeling of the Hall sensor signal is necessary. In this study, a PEC probe is designed and a numerical modeling program is written so that Hall sensor signals and coil sensor signals can be calculated simultaneously. First, a step current is used as the input current. The predicted Hall sensor signals show similar characteristics to those of the experimental signals reported by other researchers. The characteristics of the two types of signals are then analyzed and compared as the thickness of test object changes. The results show that the Hall sensor signal provides less information for evaluating the thickness of the test object than the coil sensor signal. The response signals from a pulsed input current are also calculated, and it is confirmed that an equivalent reversed signal pattern appeared after the pulse width at both signals.