• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.89-92
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• 2000

필터뱅크를 기반으로 하는 이산 웨이브렛 멀티톤(DWMT) 데이터 전송 시스템은 전송 채널의 영향으로 발생하는 심벌간 및 부채널간의 간섭 잡음에 민감하다. 본 논문에서 제안한 시간 영역 등화기(TEQ)는 전송 채널 응답의 길이를 단축시키는 TEQ-S와 전송 채널의 주파수 특성을 향상시키는 TEQ-C등 2단계로 이루어져 있다. 제안한 시간영역 등화기(TEQ)를 DWMT 데이터 전송 시스템에 적용하면 많은 간섭 성분을 감소시킬 수 있어 하드웨어적으로 보다 간단한 FEQ로도 잔여 간섭 잡음을 효과적으로 제거할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.75-77
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• 2017

디지털 제어와 사물 인터넷을 통한 네트워크 기반 제어가 확산되면서 정밀 제어를 위한 양자화 오류에 대한 고려의 중요성이 증가하고 있다. 본 연구에서는 이산 시간 출력 궤환 제어 시스템에서 제어 신호 생성에 사용되는 시스템 출력 신호에 양자화 오류가 있을 때, 리아푸노프 시스템 안정성을 보장하는 조건을 선형 행렬 부등식을 통하여 제시한다.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.74-79
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• 2002

Many control techniques have been proposed in order to improve the control performance in discrete-time domain control system. In control system using these techniques, the response-characteristic of system is dependent on the gains of the controller. Specially, There is a need to readjust the gain of controller when the response of system is changed by disturbance or load fluctuation. In this paper, I-PD controller and pre-compensator are designed by multilayer neural network. The gains of I-PD controller and pre-compensator are adjusted automatically by back propagation algorithm when the response characteristic of system is changed under a condition. Applying this control technique to the position control system using a DC servo motor as a driver, the control performance of controller is verified by the results of experiment.

• The KIPS Transactions:PartB
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• v.18B no.3
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• pp.147-156
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• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.27 no.7
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• pp.372-378
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• 2008

Voice activity detectors (VAD) are important in wireless communication and speech signal processing, In the conventional VAD methods, an expression for the likelihood ratio test (LRT) based on statistical models is derived in discrete Fourier transform (DFT) domain, Then, speech or noise is decided by comparing the value of the expression with a threshold, This paper presents a new statistical VAD method based on a signal subspace approach, The probabilistic principal component analysis (PPCA) is employed to obtain a signal subspace model that incorporates probabilistic model of noisy signal to the signal subspace method, The proposed approach provides a novel decision rule based on LRT in the signal subspace domain, Experimental results show that the proposed signal subspace model based VAD method outperforms those based on the widely used Gaussian distribution in DFT domain.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.8
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• pp.155-160
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• 2002

In this paper, a controller design for brushless linear motor is implemented. The designed controller is mainly composed of current, speed and position controller, which are carried out by the high-speed digital signal processor (DSP). In addition the PWM inverter is controlled by space voltage PWM method. This system is implemented by using 32-bit DSP (TMS320C31), a high-integrated logic device (EPM7192), and IPM (Intelligent Power Module) for compact and powerful system design. The experimental results show the effective performance of controller for the brushless linear motor.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.167-169
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• 2001

In this paper, a controller design for high speed and stiffness linear motor is implemented. The designed controller is mainly composed of speed and current controller, which are carried out by the high-speed digital signal processor(DSP). In addition the PWM inverter is controlled by space voltage PWM method. This system is implemented by using 32-bit DSP(TMS320C31), a high-integrated logic device(EPM7128), and IPM(Intelligent Power Module) for compact and powerful system design. The experimental results show the effective performance of controller for high speed and stiffness linear motor.

• Journal of KSNVE
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• v.6 no.6
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• pp.851-856
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• 1996

The active noise cancellation system showing the effective convergence and stability has been studied by simplifying the controller structures using the weighting factors of control error path to the multi-channel filtered-x LMS algorithm which needs a lot of calculations and the performance has been verified experimentally. Besides, to implement the system performance in a vehicle cabin, experimental work for selecting the suitable numbers and positions of the microphones and speakers was accomplished. Effectively combining a TMS 320C 31 main processor conducting real number calculations and having various functions with other components, the purpose-built system board for active noise cancellation has been designed and with this board, car active noise cancellation system showing maximum stable 10dB noise reduction has been obtained at the car idling conditions above 3000rpm range.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.6
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• pp.100-105
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• 2017

This work presents an ECG biometric recognition system for the purpose of biometric authentication. ECG biometric approaches are divided into two major categories, fiducial-based and non-fiducial-based methods. This paper proposes a new non-fiducial framework using discrete cosine transform and a Random Forest classifier. When using DCT, most of the signal information tends to be concentrated in a few low-frequency components. In order to apply feature vector of Random Forest, DCT feature vectors of ECG heartbeats are constructed by using the first 40 DCT coefficients. RF is based on the computation of a large number of decision trees. It is relatively fast, robust and inherently suitable for multi-class problems. Furthermore, it trade-off threshold between admission and rejection of ID inside RF classifier. As a result, proposed method offers 99.9% recognition rates when tested on MIT-BIH NSRDB.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.2 s.95
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• pp.153-163
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• 1999

본 연구에서는 격자패널을 통한 소음전달을 감소시키기 위하여 외부 구조적 가진으로부터 유발된 경량 패널의 진동을 능동 제어하는 기법을 기술한다. 최적 되먹임제어기와 적응 앞먹임제어기가 결합된 혼합형 제어기가 진동제어기로 사용된다. 되먹임제어기는 주파수 영역의 모델규명법에 의해 추출된 다중 입/출력 패널진동계 모델에 대하여 LQG 최적기법을 이용하여 감쇠능을 향상시키도록 설계된다. 앞먹임제어기는 되먹임 궤환의 결합효과를 자동적으로 보정할 수 있는 제안된 학습법칙에 기초하여 패널의 잔류진동이 최소가 되도록 적응된다. 45.7${\times}$45.7${\times}$2.54 ${cm^3}$ 벌집형상의 고강도 패널, 4개의 관성형 구동기 및 이산신호처리장치에 의해 구현된 패널 진동계에 대한 능동제어 실험을 수행해 본 결과 600Hz 주파수대역에 대한 12dB 진동저감이 이루어 질 수 있었다.