• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8B
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• pp.1460-1468
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• 2000

The goal of this research is the real-time implementation of MPEG-1 Layer III audio decoder using the fixed-point digital signal processor of TMS320C6201 The main job for this work is twofold: one is to convert floating-point operation in the decoder into fixed-point operation while maintaining the high resolution, and the other is to optimize the program to make it run in real-time with memory size as small as possible. We, especially, devote much time to the descaling module in the decoder for conversion of floating-point operation into fixed-point operation with high accuracy. The inverse modified cosine transform(IMDCT) and synthesis polyphase filter bank modules are optimized in order to reduce the amount of computation and memory size. After the optimization process, in this paper, the implemented decoder uses about 26% of maximum computation capacity of TMS320C6201. The program memory, data ROM, data RAM used in the decoder are about 6.77kwords, 3.13 kwords and 9.94 kwords, respectively. Comparing the PCM output of fixed-point computation with that of floating-point computation, we achieve the signal-to-noise ratio of more than 60 dB. A real-time operation is demonstrated on the PC using the sound I/O and host communication functions in the EVM board.

• 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.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.31 no.4
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• pp.393-401
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• 1995

In this paper, vector analysis on the novel quick torque control of Induction Motors(I.M) based on voltage-controlled type is conducted. It was very difficult to get a step response of torque when the primary voltage was selected as control input of induction motors in conventional quick torque control methods. To solve this problem, the new control method was developed using a new concept of pulse addition which can realize the stepwise torque response of a specified settling time of $\Delta$. The new method was successfully confirmed through DSP(Digital Signal Processor) system-based experiments. However, it was a little difficult to understand the control mechanism intutionally. The purpose of this paper is to provide more understanding about the quick torque control mechanism using the vector analysis.

• Journal of Power Electronics
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• v.16 no.5
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• pp.1964-1980
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• 2016

This paper presents a new simplified harmonics extraction algorithm based on the synchronous reference frame (SRF) for an indirect current controlled (ICC) three-level neutral point diode clamped (NPC) inverter-based shunt active power filter (SAPF). The shunt APF is widely accepted as one of the most effective current harmonics mitigation tools due to its superior adaptability in dynamic state conditions. In its controller, the SRF algorithm which is derived based on the direct-quadrature (DQ) theory has played a significant role as a harmonics extraction algorithm due to its simple implementation features. However, it suffers from significant delays due to its dependency on a numerical filter and unnecessary computation workloads. Moreover, the algorithm is mostly implemented for the direct current controlled (DCC) based SAPF which operates based on a non-sinusoidal reference current. This degrades the mitigation performances since the DCC based operation does not possess exact information on the actual source current which suffers from switching ripples problems. Therefore, three major improvements are introduced which include the development of a mathematical based fundamental component identifier to replace the numerical filter, the removal of redundant features, and the generation of a sinusoidal reference current. The proposed algorithm is developed and evaluated in MATLAB / Simulink. A laboratory prototype utilizing a TMS320F28335 digital signal processor (DSP) is also implemented to validate effectiveness of the proposed algorithm. Both simulation and experimental results are presented. They show significant improvements in terms of total harmonic distortion (THD) and dynamic response when compared to a conventional SRF algorithm.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.9
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• pp.103-112
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• 1999

In this paper, a speaker-independent isolated world recognition system for a car navigation system is implemented using a general digital signal processor. This paper presents a method combining SNR normalization with RAS as a noise processing method. The semi-continuous hidden markov model is adopted and TMS320C31 is used in implementing the real-time system. Recognition word set is composed of 69 command words for a car navigation system. Experimental results showed that the recognition performance has a maximum of 93.62% in case of a combination of SNR normalization and spectral subtraction, and the performance improvement rate of the system is 3.69%, Presented noise processing method showed good speech recognition performance in 5dB SNR in car environment.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.2
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• pp.138-147
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• 2009

In this paper, improved direct torque control(DTC) system for five-phase squirrel-cage induction motor(IM) is proposed. Due to the additional degrees of freedom, five-phase 1M drives present unique characteristics. Also five-phase motor drives possess many other advantages compared with the traditional three-phase motor drive system, such as reducing an amplitude of torque pulsation and increasing the reliability. The DTC method is advantageous when it is applied to the five-phase IM, because the five-phase inverter provides 32 space vectors in comparison to 8 space voltage vectors into the three-phase inverter. However, five-phase motor has structural drawback of 3rd space-harmonics current component, it is necessary to controlled 3rd harmonic current. So to control 3rd harmonic current and enhance dynamic characteristics of five-phase squirrel-cage IM drive, modified DTC method should be demanded. The characteristics and dynamic performance of traditional five-phase DTC are analyzed and new DTC for five-phase IM is presented. A more precise flux and torque control algorithm for the drives can be suggested and explained For presenting the superior performance of the proposed direct torque control, experimental results are presented using a 32-[bit] fixed point TMS320F2812 digital signal processor with 2.2[kW] induction motor.

• The Journal of the Acoustical Society of Korea
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• v.29 no.7
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• pp.458-467
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• 2010

In this paper, a new audio reproduction system was developed in which the cross-talk signals would be reasonably cancelled at an arbitrary listener position. To adaptively remove the cross-talk signals according to the listener's position, a method of tracking the listener position was employed. This was achieved using the two microphones, where the listener direction was estimated using the time-delay between the two signals from the two microphones, respectively. Moreover, room reverberation effects were taken into consideration where linear prediction analysis was involved. To remove the cross-talk signals at the left-and right-ears, the paths between the sources and the ears were represented using the KEMAR head-related transfer functions (HRTFs) which were measured from the artificial dummy head. To evaluate the usefulness of the proposed listener tracking system, the performance of cross-talk cancellation was evaluated at the estimated listener positions. The performance was evaluated in terms of the channel separation ration (CSR), a -10 dB of CSR was experimentally achieved although the listener positions were more or less deviated. A real-time system was implemented using a floating-point digital signal processor (DSP). It was confirmed that the average errors of the listener direction was 5 degree and the subjects indicated that 80 % of the stimuli was perceived as the correct directions.