• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.2
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• pp.163-170
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• 2004

Since 1960s, a container shipping volume has increased dramatically and continuous on a trend of rapid growth, and so the number of containers handled at the port increases. The position measurement of the yard crane is very important for improving the operating efficiency of the port. This paper describes the method to measure the absolute position of yard crane accurately and rapidly, using both the output pulse of an encoder and infrared sensors. The crane position is calculated by counting the output pulse of an incremental encoder, which is mounted on the wheel in the crane. By the way, the wheel slippage on rail may cause some errors in the crane position information obtained from encoder pulse, and the infrared sensor is used to compensate for errors in the crane position information. The performance of proposed method is verified on experimental results with the simulator of yard crane, the size of which is about 1/10 with the real crane.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.427-435
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• 2023

This paper proposes a method of estimating the pose of a mobile robot by using a learning model. When estimating the pose of a mobile robot, wheel encoder and inertial measurement unit (IMU) data are generally utilized. However, depending on the condition of the ground surface, slip occurs due to interaction between the wheel and the floor. In this case, it is hard to predict pose accurately by using only encoder and IMU. Thus, in order to reduce pose error even in such conditions, this paper introduces a pose estimation method based on a learning model using data of the wheel encoder and IMU. As the learning model, long short-term memory (LSTM) network is adopted. The inputs to LSTM are velocity and acceleration data from the wheel encoder and IMU. Outputs from network are corrected linear and angular velocity. Estimated pose is calculated through numerically integrating output velocities. Dataset used as ground truth of learning model is collected in various ground conditions. Experimental results demonstrate that proposed learning model has higher accuracy of pose estimation than extended Kalman filter (EKF) and other learning models using the same data under various ground conditions.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.86-92
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• 2001

Position controls are very important in semiconductor manufacturing devices, machine tools, precision measuring instruments, etc. to measure the distance of movement of moving objects in minute units and the accuracy of measurement for the moving distance in these devices affect the performance of the whole devices. Therefore, in those precision instruments, a sensing device that can measure the distance of movement with high-precision resolution is required. Thus an optical encoder that has such advantages as easy digital interface, economical price, and a resolution similar to that of laser interferometers can be used. In this paper, a interferometer-type linear scale with easy digital interface and high-resolution has been set up and measured the distance of movement based on the diffraction principle. Interference signals produced in this optical setup of the linear scale have beers digitalized through fabricated photodetectors and designed signal processing circuits. A resolution of 0.5${\mu}{\textrm}{m}$ is acquired from the experimental interferometer-type linear scale without for the movement of scales any additional dividing circuits. It is shown that from this experiment a high-resolution distance measurement device can be designed by a simple optical setup.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.228-231
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• 1987

Microprocessor-based software DDA interpolator is developed and applied to two axis contouring control of X-Y table. Developed assembly program is composed of feedrate, linear and circular DDA interpolation routines. Reference-pulse type of open-loop stepping motor control system in which the micro-computer produces a sequence of reference pulses for each axis of motion is adopted. To test performance of the developed program, X-Y table drive system based on stepping motor and shaft encoder is designed. Contouring error of the system in linear and circular path is within .+-.0.2 mm.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.4
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• pp.249-254
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• 2013

A fast multiplier and ADC are essential to process the analog signals in real time. The double-base number system(DBNS) is known as an efficient method for this purpose. The DBNS uses the numbers 2 and 3 as the base numbers simultaneously. The system has an advantage of fast multiplication, less chip area, and low power consumption compared to the binary multiplier. However, the inherent errors of the log number's intrinsic tolerance in DBNS are accumulated in a FIR digital filter, so the signal-to-noise ratio(SNR) has a tendency to be degraded. In this paper, the nonlinear encoder of ADC is designed to compensate the accumulated errors of DBNS by analysing the error distributions of various filter coefficients. The new ADC does not sacrifice its own advantages because the encoder circuits are modified only. The experiments were done with an FIR filters those were designed to have -70dB of SNR in stop band. The proposed nonlinear ADC encoder could drop the SNR to -45dB in stop band, in contrast to -35dB with the linear encoder.

• Journal of KIISE:Information Networking
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• v.35 no.3
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• pp.235-242
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• 2008

To improve video quality and coding efficiency, H.264/AVC adopted an adaptive rate control. But this method has a problem as it cannot predict an accurate quantization parameter(QP) for the first frame. The first QP is decided among four constant values by using encoder input parameters. It does not consider encoding bits, results in significant fluctuation of the image quality and decreases the average quality of the whole coded sequence. In this paper, we propose a new algorithm for the first frame QP decision in the H.264/AVC encoder. The QP is decided by the existing algorithm and the first frame is encoded. According to the encoded bits, the new initial QP is decided. We can predict optimal value because there is a linear relationship between encoded bits and the new initial QP. Next, we re-encode the first frame using the new initial QP. Experimental results show that the proposed algorithm not only achieves better quality than the state of the art algorithm, but also adopts a rate control forthe sequence that was impossible with the existing algorithm. By reducing fluctuation, subjective quality also improved.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.1021-1022
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• 2010

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.80-87
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• 2017

This paper proposes a pre-processing algorithm to improve perceptual video coding efficiency which decomposes an input frame via a sub-band decomposition, and suppresses only high frequency band(s) having low visual sensitivity. First, we decompose the input frame into several frequency subbands by a linear sub-band decomposition. Next, high frequency subband(s) which is rarely recognized by human visual system (HVS) is suppressed by applying relatively small gain(s). Finally, the high frequency suppressed frame is compressed by a specific video encoder. We can find from the experimental results that if comparing before-use and after-use of the proposed pre-processing prior to the encoder, no visual difference is shown. Also, the proposed algorithm achieves bit-saving of 13.12% on average in a H.264 video encoder.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.670-677
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• 2019

As the presence of background noise in acoustic signal degrades the performance of speech or acoustic event recognition, it is still challenging to extract noise-robust acoustic features from noisy signal. In this paper, we propose a combined structure of Wasserstein Generative Adversarial Network (WGAN) and MultiTask AutoEncoder (MTAE) as deep learning architecture that integrates the strength of MTAE and WGAN respectively such that it estimates not only noise but also speech features from noisy acoustic source. The proposed MTAE-WGAN structure is used to estimate speech signal and the residual noise by employing a gradient penalty and a weight initialization method for Leaky Rectified Linear Unit (LReLU) and Parametric ReLU (PReLU). The proposed MTAE-WGAN structure with the adopted gradient penalty loss function enhances the speech features and subsequently achieve substantial Phoneme Error Rate (PER) improvements over the stand-alone Deep Denoising Autoencoder (DDAE), MTAE, Redundant Convolutional Encoder-Decoder (R-CED) and Recurrent MTAE (RMTAE) models for robust speech recognition.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.9A
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• pp.917-923
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• 2008

The D-STTD system obtains the diversity gain through the STTD scheme and the Multiplexing gain through parallel structure of the encoder using the STTD scheme known Alamouti Code. We are difficult to use Combining scheme of the STTD scheme for the D-STTD detection in the decoder because the D-STTD system transmits mutually different data in each other STTD encoder for multiplexing gain. Therefore, in this paper we combine the D-STTD system with Linear algorithm, SIC algorithm and OSIC algorithm known multiplexing detection scheme based on MMSE scheme and compare the performance of each system. And we propose the detection scheme of the D-STTD using MAP Algorithm and analyze the performance of each system. The simulation results show that the detector using iterative algorithm has better performance than Linear MMSE Detector. Especially, we show that the detector using MAP algorithm outperforms conventional detector.