• JSTS:Journal of Semiconductor Technology and Science
• /
• v.4 no.3
• /
• pp.228-239
• /
• 2004

In this paper, an analytical model accounting for the quantum effects in MOSFETs has been developed to study the behaviour of $high-{\kappa}$ dielectrics and to calculate the threshold voltage of the device considering two dielectrics gate stack. The effect of variation in gate stack thickness and permittivity on surface potential, inversion layer charge density, threshold voltage, and $I_D-V_D$ characteristics have also been studied. This work aims at presenting a relation between the physical gate dielectric thickness, dielectric constant and substrate doping concentration to achieve targeted threshold voltage, together with minimizing the effect of gate tunneling current. The results so obtained are compared with the available simulated data and the other models available in the literature and show good agreement.

• The Journal of the Acoustical Society of Korea
• /
• v.21 no.7
• /
• pp.641-647
• /
• 2002

In this paper, we propose the reduction method of the number of parameters in the segmental-feature HMM using trend quantization method. The proposed method shares the trend information of the polynomial trajectories by quantization. The trajectory is obtained by the sequence of feature vectors of speech signals and can be divided by trend and location information. The trend indicates the variation of consequent frame features, while the location points to the positional difference of the trajectories. Since the trend occupies the large portion of SFHMM, if the trend is shared, the number of parameters maybe decreases. To exploit the proposed system the experiments are performed on TIMIT corpus. The experimental results show that the performance of the proposed system is roughly similar to that of previous system. Therefore, the proposed system can be considered one of parameter reduction method.

• Journal of the Institute of Convergence Signal Processing
• /
• v.17 no.2
• /
• pp.67-70
• /
• 2016

This paper discusses a graphic user interface(GUI) for the concealed target segmentation. The human subject hiding a metal gun is captured by the passive millimeter wave(MMW) imaging system. The imaging system operates on the regime of 8 mm wavelength. The MMW image is analyzed by the multi-level segmentation to segment and identify a concealed weapon under clothing. The histogram of the passive MMW image is modeled with the Gaussian mixture distribution. LBG vector quantization(VQ) and expectation and maximization(EM) algorithms are sequentially applied to segment the body and the object area. In the experiment, the GUI is implemented by the MFC(Microsoft Foundation Class) and the OpenCV(Computer Vision) libraries and tested in real-time showing the efficiency of the system.

• Journal of Power Electronics
• /
• v.17 no.3
• /
• pp.686-694
• /
• 2017

Due to the wide use of electronic products, digitally controlled DC-DC converters are attracting more and more attention in recent years. However, digital control strategies may introduce undesirable Limit Cycle Oscillation (LCO) due to quantization effects in the Analog-to-Digital Converter (ADC) and Digital Pulse Width Modulator (DPWM). This results in decreases in the quality of the output voltage and the efficiency of the system. Meanwhile, even if the resolution of the DPWM is finer than that of the ADC, LCO may still exist due to improper parameters of the digital compensator. In order to discover how LCO is generated, the state space averaging model is applied to derive equilibrium equations of a digital PID controlled DC-DC converter in this paper. Furthermore, the influences of the parameters of the digital PID compensator, and the resolutions of the ADC and DPWM on LCO are studied in detail. The amplitude together with the period of LCO as well as the corresponding PID parameters are obtained. Finally, MATLAB/Simulink simulations and FPGA verifications are carried out and no-LCO conditions are obtained.

• The Journal of the Acoustical Society of Korea
• /
• v.18 no.7
• /
• pp.17-22
• /
• 1999

In this paper we propose a new mixed method of LDA and tri-tone model to predict Korean prosodic break indices(PBI) for a given utterance. PBI can be used as an important cue of syntactic discontinuity in continuous speech recognition(CSR). The model consists of three steps. At the first step, PBI was predicted with the information of syllable and pause duration through the linear discriminant analysis (LDA) method. At the second step, syllable tone information was used to estimate PBI. In this step we used vector quantization (VQ) for coding the syllable tones and PBI is estimated by tri-tone model. In the last step, two PBI predictors were integrated by a weight factor. The proposed method was tested on 200 literal style spoken sentences. The experimental results showed 72% accuracy.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.27 no.12
• /
• pp.1933-1939
• /
• 1990

In this paper, we proposed an algorithm which used backtracking method to get time information, and it be modelled DMS (Dynamic Multisection) by feature vectors and time information whic are represented to similiar feature in word patterns spoken during continuous time domain, for Korean Speech recognition by independent speaker using DMS. Each state of model is represented time sequence, and have time information and feature vector. Typical feature vector is determined as the feature vector of each state to minimize the distance between word patterns. DDD Area names are selected as recognition wcabulary and 12th LPC cepstrum coefficients are used as the feature parameter. State of model is made 8 multisection and is used 0.2 as weight for time information. Through the experiment result, recognition rate by DMS model is 94.8%, and it is shown that this is better than recognition rate (89.3%) by MSVQ(Multisection Vector Quantization) method.

• Proceedings of the KSPS conference
• /
• 2005.04a
• /
• pp.87-90
• /
• 2005

Today's state-of the-art speech recognition systems typically use continuous distribution hidden Markov models with the mixtures of Gaussian distributions. To obtain higher recognition accuracy, the hidden Markov models typically require huge number of Gaussian distributions. Such speech recognition systems have problems that they require too much memory to run, and are too slow for large applications. Many approaches are proposed for the design of compact acoustic models. One of those models is subspace distribution clustering hidden Markov model. Subspace distribution clustering hidden Markov model can represent original full-space distributions as some combinations of a small number of subspace distribution codebooks. Therefore, how to make the codebook is an important issue in this approach. In this paper, we report some experimental results on various quantization methods to make more accurate models.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2006.05a
• /
• pp.109-112
• /
• 2006

본 논문은 클래스들의 대표값들과 입력 벡터와의 거리를 사용한 새로운 퍼지 학습법칙을 제안한다. 이 새로운 퍼지 학습을 supervised IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망에 적용하였다. 이 새로운 신경회로망은 안정성을 유지하면서도 유연성을 가지고 있다. iris 데이터를 사용하여 테스트한 결과 supervised IAFC 신경회로망 4는 오류 역전파 신경회로망과 LVQ 알고리즘보다 성능이 우수하였다.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.34S no.9
• /
• pp.84-90
• /
• 1997

We proposed a new bit-rate control algorithm to improve MPEG-2 video software encoder. Bit-rate Control plays an improtant role in picture quality of MPEG-2 encoder. To achieve better encoding performance such as controlling picture quality and using bity properly, we proposed a MPEG-2 bit-rate control algorithm using linear average Step-Size. Using a benchmark Program, we compared our algorithm with MPEG-2 Test Model 5. Our proposed algorithm showed better Bit-Rate Control with respect to used bits, picture quality.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.7
• /
• pp.11-20
• /
• 2024

In this paper, we propose the BAG (Bit-width Aware Generator) and the Intermediate Layer Knowledge Distillation using Channel-wise Attention to reduce the knowledge gap between a quantized network, a full-precision network, and a generator in GDFQ (Generative Data-Free Quantization). Since the generator in GDFQ is only trained by the feedback from the full-precision network, the gap resulting in decreased capability due to low bit-width of the quantized network has no effect on training the generator. To alleviate this problem, BAG is quantized with same bit-width of the quantized network, and it can generate synthetic images, which are effectively used for training the quantized network. Typically, the knowledge gap between the quantized network and the full-precision network is also important. To resolve this, we compute channel-wise attention of outputs of convolutional layers, and minimize the loss function as the distance of them. As the result, the quantized network can learn which channels to focus on more from mimicking the full-precision network. To prove the efficiency of proposed methods, we quantize the network trained on CIFAR-100 with 3 bit-width weights and activations, and train it and the generator with our method. As the result, we achieve 56.14% Top-1 Accuracy and increase 3.4% higher accuracy compared to our baseline AdaDFQ.