• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.1
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• pp.23-30
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• 2010

The H.264/AVC is increasingly used in broadcast video applications such as Internet Protocol television (IPTV), digital multimedia broadcasting (DMB) because of high compression performance. But the H.264/AVC coded video can be delivered to the widespread end-user equipment for MPEG-2 after transcoding between this video standards. This paper suggests a new transcoding algorithm for H.264/AVC to MPEG-2 transcoder that uses motion vector clustering in order to reduce the complexity without loss of video quality. The proposed method is exploiting the motion information gathered during h.264 decoding stage. To reduce the search space for the MPEG-2 motion estimation, the predictive motion vector is selected with a least distortion of the candidated motion vectors. These candidate motion vectors are considering the correlation of direction and distance of motion vectors of variable blocks in H.264/AVC. And then the best predictive motion vector is refined with full-search in ${\pm}2$ pixel search area. Compared with a cascaded decoder-encoder, the proposed transcoder achieves computational complexity savings up to 64% with a similar PSNR at the constant bitrate(CBR).

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.10
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• pp.53-60
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• 2009

Due to increased integration density and reduced threshold voltages, leakage current reduction becomes important in the semiconductor IC design for low power consumption. In a combinational logic circuit, the leakage current in the standby state depends on the values of the input. In this research, we developed a new input vector control method to minimize the leakage power. A new efficient algorithm is developed to find the minimal leakage vector. It can reduce the leakage current by 15.7% from the average leakage current and by 6.7% from the results of simulated evolution method during standby or idle states for a set of benchmark circuits. The minimal leakage input vector, with idle input signal, can also reduce the leakage current by 6.8% from the average leakage current and by 3.2% from the results of simulated evolution method for sequential circuits.

• Journal of Power Electronics
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• v.16 no.4
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• pp.1336-1345
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• 2016

A modified space vector pulse width modulation (SVPWM) strategy based on vector space decomposition and its equivalent carrier-based PWM realization are proposed in this paper, which is suitable for six-phase asymmetrical dual stator induction machines (DSIMs). A DSIM is composed of two sets of symmetrical three-phase stator windings spatially shifted by 30 electrical degrees and a squirrel-cage type rotor. The proposed SVPWM technique can reduce torque ripples and suppress the harmonic currents flowing in the stator windings. Above all, the equivalent relationship between the proposed SVPWM technique and the carrier-based PWM technique has been demonstrated, which allows for easy implementation by a digital signal processor (DSP). Simulation and experimental results, carried out separately on a simulation system and a 3.0 kW DSIM prototype test bench, are presented and discussed.

• Journal of Information Technology Services
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• v.10 no.2
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• pp.235-245
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• 2011

Early criticality prediction models that determine whether a design entity is fault-prone or not are becoming more and more important as software development projects are getting larger. Effective predictions can reduce the system development cost and improve software quality by identifying trouble-spots at early phases and proper allocation of effort and resources. Many prediction models have been proposed using statistical and machine learning methods. This paper builds a prediction model using Support Vector Machine(SVM) which is one of the most popular modern classification methods and compares its prediction performance with a well-known prediction model, BackPropagation neural network Model(BPM). SVM is known to generalize well even in high dimensional spaces under small training data conditions. In prediction performance evaluation experiments, dimensionality reduction techniques for data set are not used because the dimension of input data is too small. Experimental results show that the prediction performance of SVM model is slightly better than that of BPM and polynomial kernel function achieves better performance than other SVM kernel functions.

• International Journal of Control, Automation, and Systems
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• v.5 no.2
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• pp.155-160
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• 2007

A fault detection and isolation (FDI) problem is considered for inertial sensors, such as gyroscopes and accelerometers and a new FDI method for double faults is proposed using reduced-order parity vector. The reduced-order parity vector (RPV) algorithm enables us to isolate double faults with 7 sensors. Averaged parity vector is used to reduce false alarm and wrong isolation, and to improve correct isolation. The RPV algorithm is analyzed by Monte-Carlo simulation and the performance is given through fault detection probability, correct isolation probability, and wrong isolation probability.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2287-2290
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• 2003

In this paper, an image vector quantization method is proposed not only to improve the compression ratio but also to reduce the computation cost. The proposed method could save the computation cost of codebook generation and encoding by using the modified LBG algorithm of Partial Search Partial Distortion (PSPD) in wavelet domain, by which the code book was constructed together with the partial codebook search, the partial code vector elements, and the interruption criterion. We have designed and implemented the vector quantizer to verify the improvement in reducing compression ratio in encoding processing and reducing the computation cost.

• Management Science and Financial Engineering
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• v.16 no.1
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• pp.81-117
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• 2010

Semi-supervised learning incorporates unlabeled examples, whose labels are unknown, as well as labeled examples into learning process. Although transductive support vector machine (TSVM), one of semi-supervised learning models, was proposed about a decade ago, its application to large-scaled data has still been limited due to its high computational complexity. Our previous research addressed this limitation by introducing a branch-and-bound algorithm for finding an optimal solution to TSVM. In this paper, we propose three new techniques to enhance the performance of the branch-and-bound algorithm. The first one tightens min-cut bound, one of two bounding strategies. Another technique exploits a graph-based approximation to a support vector machine problem to avoid the most time-consuming step. The last one tries to fix the labels of unlabeled examples whose labels can be obviously predicted based on labeled examples. Experimental results are presented which demonstrate that the proposed techniques can reduce drastically the number of subproblems and eventually computational time.

• MALSORI
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• no.63
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• pp.113-124
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• 2007

In this paper, we propose a use of confidence vector as an intermediate input feature for multi-stage based speech recognition architecture to improve recognition accuracy. A multi-stage speech recognition structure is introduced as a method to reduce the computational complexity of the decoding procedure and then accomplish faster speech recognition. Conventional multi-stage speech recognition is usually composed of three stages, acoustic search, lexical search, and acoustic re-scoring. In this paper, we focus on improving the accuracy of the lexical decoding by introducing a confidence vector as an input feature instead of phoneme which was used typically. We take experimental results on 220K Korean Point-of-Interest (POI) domain and the experimental results show that the proposed method contributes on improving accuracy.

• Proceedings of the KIPE Conference
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• 2018.07a
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• pp.63-65
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• 2018

This paper presents a DTC-PWM(Direct Torque Control-Pluse Width Modulation) method of PMSM(Permanent Magnet Synchronous Motor) using the flux-torque hysteresis band. In order to keep the flux and torque error of the PMSM within the hysteresis band, the optimal PWM duty ratio is calculated by the error of the flux and torque with the flux and torque vector of the selected voltage vector. According to the flux duty ratios and the torque duty ratios, the optimized duty ratio to reduce the errors is selected by the calculated duty ratios. In the proposed method, the selected voltage vector is divided into d-q axis components with a simple method. And the flux duty ratios and torque duty ratios are estimated by the applied voltage vector. The proposed DTC-PWM for PMSM was verified by computer simulation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.17-24
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• 1994

This paper is a study on Korean word recognition and suggest the method that stabilizes the state-transition in the HMM by applying the `inertia' to the feature vector sequences. In order to reduce the quantized distortion considering probability distribution of input vectors, we used SOFM, an unsupervised learning method, as a vector quantizer, By applying inertia to the feature vector sequences, the overlapping of probability distributions for the response path of each word on the self organizing feature map can be reduced and the state-transition in the Hmm can be Stabilized. In order to evaluate the performance of the method, we carried out experiments for 50 DDD area names. The results showed that applying inertia to the feature vector sequence improved the recognition rate by 7.4% and can make more HMMs available without reducing the recognition rate for the SOFM having the fixed number of neuron.