• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.1
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• pp.90-97
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• 1991

A stepping motor can be driven with open-loop or closed-loop control. The major disadvantage of open-loop control is that it is subjected to resonance and instability in certain speed range, and that there is no way to check stalling or error in position. In this paper, a closed-loop control system consisting of a microcomputer, a hybrid stepping motor, a drive, a lead screw, and an encoder which is used as a position sensor is developed. A control program is programmed in assembly language for real time control and the versatile interface adapter(VIA) is used as the interface with the microcomputer. The experimental results of the stepping motor were eliminated on all kinds of inertia load, and maximum start stop pulse rate and maximum slewing pulse rate were also increased about twice respectively.

• The Transactions of the Korean Institute of Power Electronics
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• v.8 no.4
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• pp.336-343
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• 2003

Generally, brushless DC motor(BLDCM) driving system uses hall sensors or encoders as the mechanical position or speed sensors. It is necessary to achieve the information's of rotor position for driving trapezoidal type brushless DC motor without any position sensor. This paper proposes a sensorless driving system with absolute rotor position detecting circuit which acquires both commutating phase and commutating time by analyzing motor phase voltages. Proposed system is applied to a 10k[W] rating motor which actually used in Hybrid Electric Vehicles. The experimental results will show the validity of the proposed system and the practical use of proposed sensorless drive.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.4
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• pp.740-749
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• 1994

This paper presents method for improving the performance of the VSELP speech coder. The hybrid method is employed for pitch period searching. Pitch searching time is reduced and pitch detection error, caused by quantization error of excitation signal of encoder in VSELP coder, is reduced by this method. This paper also adopts a pitch period enhancement filter and an adaptive first order filter. In this result, pitch period searching time is reduced to 26%, and MOS of reconstructed speech signal is increased by 3.19 to 4.04.

• International Journal of Automotive Technology
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• v.7 no.6
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• pp.729-739
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• 2006

This paper describes an active fault-tolerant control system for an induction motor drive that propels an Electrical Vehicle(EV) or a Hybrid one(HEV). The proposed system adaptively reorganizes itself in the event of sensor loss or sensor recovery to sustain the best control performance given the complement of remaining sensors. Moreover, the developed system takes into account the controller transition smoothness in terms of speed and torque transients. In this paper which is the sequel of (Diallo et al., 2004), we propose to introduce more advanced and intelligent control techniques to improve the global performance of the fault-tolerant drive for automotive applications(e.g. EVs or HEVs). In fact, two control techniques are chosen to illustrate the consistency of the proposed approach: sliding mode for encoder-based control; and fuzzy logics for sensorless control. Moreover, the system control reorganization is now managed by a fuzzy decision system to improve the transitions smoothness. Simulations tests, in terms of speed and torque responses, have been carried out on a 4-kW induction motor drive to evaluate the consistency and the performance of the proposed fault-tolerant control approach.

• Journal of Internet Computing and Services
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• v.22 no.3
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• pp.9-16
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• 2021

This study aims to develop a human activity recognition (HAR) system as a Deep-Learning (DL) classification model, distinguishing various human activities. We solely rely on the signals from a wristband accelerometer worn by a person for the user's convenience. 3-axis sequential acceleration signal data are gathered within a predefined time-window-slice, and they are used as input to the classification system. We are particularly interested in developing a Deep-Learning model that can outperform conventional machine learning classification performance. A total of 13 activities based on the laboratory experiments' data are used for the initial performance comparison. We have improved classification performance using the Convolutional Neural Network (CNN) combined with an auto-encoder feature reduction and parameter tuning. With various publically available HAR datasets, we could also achieve significant improvement in HAR classification. Our CNN model is also compared against Recurrent-Neural-Network(RNN) with Long Short-Term Memory(LSTM) to demonstrate its superiority. Noticeably, our model could distinguish both general activities and near-identical activities such as sitting down on the chair and floor, with almost perfect classification accuracy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.1-11
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• 2007

H.264/AVC uses variable block sizes to achieve significant coding gain. It has 7 different coding modes having different motion compensation block sizes in Inter slice, and 2 different intra prediction modes in Intra slice. This fine-tuned new coding feature has achieved far more significant coding gain compared with previous video coding standards. However, extremely high computational complexity is required when rate-distortion optimization (RDO) algorithm is used. This computational complexity is a major problem in implementing real-time H.264/AVC encoder on computationally constrained devices. Therefore, there is a clear need for complexity reduction algorithm of H.264/AVC such as fast mode decision. In this paper, we propose a fast mode decision with early $P8\times8$ mode rejection based on block size activity using large block history map (LBHM). Simulation results show that without any meaningful degradation, the proposed method reduces whole encoding time on average by 53%. Also the hybrid usage of the proposed method and the early SKIP mode decision in H.264/AVC reference model reduces whole encoding time by 63% on average.

• Phonetics and Speech Sciences
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• v.11 no.1
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• pp.41-49
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• 2019

An end-to-end speech recognition model consisting of a single integrated neural network model was recently proposed. The end-to-end model does not need several training steps, and its structure is easy to understand. However, it is difficult to understand how the model recognizes speech internally. In this paper, we visualized and analyzed the attention-based end-to-end model to elucidate its internal mechanisms. We compared the acoustic model of the BLSTM-HMM hybrid model with the encoder of the end-to-end model, and visualized them using t-SNE to examine the difference between neural network layers. As a result, we were able to delineate the difference between the acoustic model and the end-to-end model encoder. Additionally, we analyzed the decoder of the end-to-end model from a language model perspective. Finally, we found that improving end-to-end model decoder is necessary to yield higher performance.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.31-43
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• 2022

Traditional video compression has developed so far based on hybrid compression methods through motion prediction, residual coding, and quantization. With the rapid development of technology through artificial neural networks in recent years, research on image compression and video compression based on artificial neural networks is also progressing rapidly, showing competitiveness compared to the performance of traditional video compression codecs. In this paper, a new method capable of improving the performance of such an artificial neural network-based video compression model is presented. Basically, we take the rate-distortion optimization method using the auto-encoder and entropy model adopted by the existing learned video compression model and shifts some components of the latent information that are difficult for entropy model to estimate when transmitting compressed latent representation to the decoder side from the encoder side, and finally compensates the distortion of lost information. In this way, the existing neural network based video compression framework, MFVC (Motion Free Video Compression) is improved and the BDBR (Bjøntegaard Delta-Rate) calculated based on H.264 is nearly twice the amount of bits (-27%) of MFVC (-14%). The proposed method has the advantage of being widely applicable to neural network based image or video compression technologies, not only to MFVC, but also to models using latent information and entropy model.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.2
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• pp.157-165
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• 2002

This paper presents the implementation of the speed control system for 3 phase induction motor using PD controller and neural networks. The PD controller is used to control the motor and to train neural networks at the first time. And neural networks are widely used as controllers because of a nonlinear mapping capability, we used feedforward neural networks(FNN) in order to simply design the speed control system of the 3 phase induction motor. Neural networks are tuned online using the speed reference, actual speed measured from an encoder and control input current to motor. PD controller and neural networks are applied to the speed control system for 3 phase induction motor, are compared with PI controller through computer simulation and experiment respectively. The results are illustrated that the output of the PD controller is decreased and feedforward neural networks act main controller, and the proposed hybrid controllers show better performance than the PI controller in abrupt load variation and the precise control is possible because the steady state error can be minimized by training neural networks.

• Journal of Communications and Networks
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• v.2 no.1
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• pp.76-84
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• 2000

Communication systems, including cell-based mobile communication systems, multiple satellite communication systems of multi-beam satellite systems, require reliable handoff methods between cell-to-cell, satellite-to-satellite of beam-to-team, respectively. Recent measurement of a CDMA cellular system indicates that the system is in handoff at about 35% to 70% of an average call period. Therefore, system reliability during handoff is one of the major system performance parameters and eventually becomes a factor in the overall system capacity. This paper presents novel and improved techniques for handoff in cellular communications, multi-beam and multi-satellite systems that require handoff during a session. this new handoff system combines the soft handoff mechanism currently implemented in the IS-95 CDMA with code and packet diversity combining techniques and an iterative decoding algorithm (Turbo Coding). the Turbo code introduced by Berrou et all. has been demonstrated its remarkable performance achieving the near Shannon channel capacity [1]. Recently. Turbo codes have been adapted as the coding scheme for the data transmission of the third generation international cellular communication standards : UTRA and CDMA 2000. Our proposed encoder and decoder schemes modified from the original Turbo code is suitable for the code and packet diversity combining techniques. this proposed system provides not only an unprecedented coding gain from the Turbo code and it iterative decoding, but also gain induced by the code and packet diversity combining technique which is similar to the hybrid Type II ARQ. We demonstrate performance improvements in AWGN channel and Rayleigh fading channel with perfect channel state information (CSI) through simulations for at low signal to noise ratio and analysis using exact upper bounding techniques for medium to high signal to noise ratio.