• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.183-186
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• 2002

최근 음성인식의 인식 단위로서 문맥의존 음향 모델이 널리 사용되고 있다. 이는 음소의 음향학적 특징, 즉 선행 및 후행음소에 의한 중심 음소의 변이음 모델이 문맥독립 모델보다 좀 더 정확하게 모델링 될 수 있기 때문이다. 하지만 강건한 문맥의존 음향 모델을 작성하기 위해서는 모델 파라미터의 병합(tying)과 미지의 문맥(unseen context)의 처리를 위한 좀더 정교한 해결 방법이 필요하다. 따라서 본 논문에서는 이점을 고려하여 음향학적 특징과 언어학적 특징을 결합하여 상태 분할을 수행할 수 있도록 SSS(Successive State Splitting) 알고리즘의 문맥 방향 상태 분할에 음소결정트리를 접목한 HM-Net(Hidden Markov Network) 구조 결정법을 도입하였다. 또한 HM-Net은 연속적인 상태 분할에 의해 한국어에서 많이 발생하는 변이음들을 효과적으로 모델링 할 수 있다는 점을 고려하여 본 연구실에서 기존에 사용하던 48 유사음소 단위에서 문맥의존 음향 모델 작성에 불필요한 변이음을 제거하여 39 유사음소 단위를 재 정의하였다. 도입한 방법과 새로 정의한 유사음소 단위의 유효성을 확인하기 위해 고립 단어, 4연속 숫자음, 연속 음성인식에 대해 인식 실험을 수행한 결과, 모든 실험에서 재 정의한 39 유사음소 단위가 문맥종속형 HM-Net 음향모델을 이용한 한국어 음성인식에 효과적임을 확인할 수 있었다. 특히 연속 음성인식 실험의 경우, 기존의 48 유사음소 단위보다 평균 $15.08\%$의 인식률 향상이 있었다.

• Journal of the Microelectronics and Packaging Society
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• v.31 no.2
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• pp.28-35
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• 2024

Corrosion inside electronic packages significantly impacts the system performance and reliability, necessitating non-destructive diagnostic techniques for system health management. This study aims to present a non-destructive method for assessing corrosion in copper interconnects using the Smith chart, a tool that integrates the magnitude and phase of complex impedance for visualization. For the experiment, specimens simulating copper transmission lines were subjected to temperature and humidity cycles according to the MIL-STD-810G standard to induce corrosion. The corrosion level of the specimen was quantitatively assessed and labeled based on color changes in the R channel. S-parameters and Smith charts with progressing corrosion stages showed unique patterns corresponding to five levels of corrosion, confirming the effectiveness of the Smith chart as a tool for corrosion assessment. Furthermore, by employing data augmentation, 4,444 Smith charts representing various corrosion levels were obtained, and artificial intelligence models were trained to output the corrosion stages of copper interconnects based on the input Smith charts. Among image classification-specialized CNN and Transformer models, the ConvNeXt model achieved the highest diagnostic performance with an accuracy of 89.4%. When diagnosing the corrosion using the Smith chart, it is possible to perform a non-destructive evaluation using electronic signals. Additionally, by integrating and visualizing signal magnitude and phase information, it is expected to perform an intuitive and noise-robust diagnosis.

• The Journal of the Acoustical Society of Korea
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• v.21 no.8
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• pp.735-743
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• 2002

In this paper, we propose three new text-independent speaker identification methods. At first, to exclude the frames not having enough features of speaker's vocal from calculation of the maximum likelihood, we propose the FS(Frame Selection) method. This approach selects the important frames by evaluating the difference between the biggest likelihood and the second in each frame, and uses only the frames in calculating the score of likelihood. Our secondly proposed, called the Hybrid, is a combined version of the FS and WMR(Weighting Model Rank). This method determines the claimed speaker using exponential function weights, instead of likelihood itself, only on the selected frames obtained from the FS method. The last proposed, called MWMR (Modified WMR), considers both original likelihood itself and its relative position, when the claimed speaker is determined. It is different from the WMR that take into account only the relative position of likelihood. Through the experiments of the speaker identification, we show that the all the proposed have higher identification rates than the ML. In addition, the Hybrid and MWMR have higher identification rate about 2% and about 3% than WMR, respectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.34-40
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• 2008

This paper presents a vision based walking robot control system using hand shape recognition. To recognize hand shapes, the accurate hand boundary needs to be tracked in image obtained from moving camera. For this, we use an active contour model-based tracking approach with mean shift which reduces dependency of the active contour model to location of initial curve. The proposed system is composed of four modules: a hand detector, a hand tracker, a hand shape recognizer and a robot controller. The hand detector detects a skin color region, which has a specific shape, as hand in an image. Then, the hand tracking is performed using an active contour model with mean shift. Thereafter the hand shape recognition is performed using Hue moments. To assess the validity of the proposed system we tested the proposed system to a walking robot, RCB-1. The experimental results show the effectiveness of the proposed system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.12B
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• pp.758-766
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• 2007

IStreaming services with a client-server architecture have scalability problem because a server cannot accomodate clients more than its processing capability. This paper introduces a case study for implementing H.264 streaming system based on P2P architecture in order to provide scalable and stable broadcast streaming services over the internet. The prototype system called OmniCast264 consists of the H.264 encoding server, the streaming server, the proxy server, and peer nodes. The proxy server dynamically manages placement of the peer nodes on the P2P network. Omnicast264 has the concepts of distributed streaming loads, real-time playback, error-robustness and modularity. Thus, it can provide large-scale broadcast streaming services. Finally, we have built P2P streaming systems with 12 PCs connected serially or in parallel. The experiment shows that OmniCast264 can provide real-time playback.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.10
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• pp.8-16
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• 2010

An orthogonal frequency division multiplexing (OFDM) system is a special case of multicarrier transmission, where a single data stream is transmitted over a number of lower-rate subcarriers. One of the main reasons to use OFDM is to increase robustness against frequency-selective fading or narrowband interference. However, in the radio systems the distortion introduced by high power amplifiers (HPA's) such as traveling wave tube amplifier (TWTA) considered in this paper, is also critical. Since the signal amplitude of the OFDM system is Rayleigh-distributed, the performance of the OFDM system is significantly degraded by the nonlinearity of the HPA in the OFDM transmitter. In this paper, we propose a simplicial canonical piecewise-linear (SCPWL) model based digital predistorter to compensate for nonlinear distortion introduced by an HPA in an OFDM system. Computer simulation is carried on an OFDM system under additive white Gaussian noise (AWGN) channels with 16-QAM and 64-QAM modulation schemes and modulator/demodulator implemented with 1024-point FFT/IFFT. The simulation results demonstrate that the proposed predistorter achieves significant performance improvement by effectively compensating for the nonlinearity introduced by the HPA.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.2
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• pp.9-17
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• 2010

Recently, there has been much interest in orthogonal frequency division multiplexing (OFDM) for next generation wireless wideband communication systems. OFDM is a special case of multicarrier transmission, where a single data stream is transmitted over a number of lower-rate subcarriers. One of the main reasons to use OFDM is to increase robustness against frequency-selective fading or narrowband interference. However, in the radio systems it is also important to distortion introduced by high power amplifiers (HPA's) such as solid state power amplifier (SSPA) considered in this paper. Since the signal amplitude of the OFDM system is Rayleigh-distributed, the performance of the OFDM system is significantly degraded by the nonlinearity of the HPA in the OFDM transmitter. In this paper, we propose a canonical piecewise-linear (PWL) model based digital predistorter to prevent signal distortion and spectral re-growth due to the high peak-to-average power ratio (PAPR) of OFDM signal and the nonlinearity of HPA's. Computer simulation on an OFDM system under additive white Gaussian noise (AWGN) channels with QPSK, 16-QAM and 64-QAM modulation schemes and modulator/demodulator implemented with 1024-point FFT/IFFT, demonstrate that the proposed predistorter achieves significant performance improvement by effectively compensating for the nonlinearity introduced by the SSPA.

• Korean Journal of Remote Sensing
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• v.36 no.2_1
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• pp.139-153
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• 2020

Using a computationalfluid dynamics(CFD) model, thisstudy evaluated the representativeness of an air quality monitoring system (AQMS) in an urban area and presented a methodology to determine the suitable AQMS locations for specific purposes. For this, we selected a 1.6 km × 1.6 km area around the Eunpyeong-gu AQMS (AQMS 111181) as a target area. We conducted simulationsfor two emission scenarios (scenario one: air pollutants transported from inflow boundaries, scenario two: air pollutants emitted from roads). Urban airflows were markedly influenced by mountainous terrain located in the northeast and southeast of the target area, and complicated airflow patterns occurred around the buildings. The distributions of air pollutants were dependent on the terrain (mountain) in scenario one, but the road location and building height in scenario 2. We evaluated whether the AQMS could represent the air quality in the target area based on the simulations for two scenarios. The concentrations simulated at the AQMS were similar in magnitude to the layer mean concentrations, which indicated good representativeness for the air quality in the target area. We also suggested which locations were suitable for different measurement purposes (hot spots, clean zones, average zones, shelter zones, equi-background zones).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.1-8
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• 2008

The belief propagation method that has been studied recently yields good performance in disparity extraction. The method in which a target function is modeled as an energy function based on Markov random field(MRF), solves the stereo matching problem by finding the disparity to minimize the energy function. MRF models provide robust and unified framework for vision problem such as stereo and image restoration. the belief propagation method produces quite correct results, but it has difficulty in real time implementation because of higher computational complexity than other stereo methods. To relieve this problem, in this paper, we propose a fast algorithm of the belief propagation method. Energy function consists of a data term and a smoothness tern. The data term usually corresponds to the difference in brightness between correspondences, and smoothness term indicates the continuity of adjacent pixels. Smoothness information is created from messages, which are assigned using four different message arrays for the pixel positions adjacent in four directions. The processing time for four message arrays dominates 80 percent of the whole program execution time. In the proposed method, we propose an algorithm that dramatically reduces the processing time require in message calculation, since the message.; are not produced in four arrays but in a single array. Tn the last step of disparity extraction process, the messages are called in the single integrated array and this algorithm requires 1/4 computational complexity of the conventional method. Our method is evaluated by comparing the disparity error rates of our method and the conventional method. Experimental results show that the proposed method remarkably reduces the execution time while it rarely increases disparity error.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.167-183
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• 2023

In the recent field of recommendation systems, various studies have been conducted to model sparse data effectively. Among these, GLocal-K(Global and Local Kernels for Recommender Systems) is a research endeavor combining global and local kernels to provide personalized recommendations by considering global data patterns and individual user characteristics. However, due to its utilization of kernel tricks, GLocal-K exhibits diminished performance on highly sparse data and struggles to offer recommendations for new users or items due to the absence of side information. In this paper, to address these limitations of GLocal-K, we propose the GEase-K (Global and EASE kernels for Recommender Systems) model, incorporating the EASE(Embarrassingly Shallow Autoencoders for Sparse Data) model and leveraging side information. Initially, we substitute EASE for the local kernel in GLocal-K to enhance recommendation performance on highly sparse data. EASE, functioning as a simple linear operational structure, is an autoencoder that performs highly on extremely sparse data through regularization and learning item similarity. Additionally, we utilize side information to alleviate the cold-start problem. We enhance the understanding of user-item similarities by employing a conditional autoencoder structure during the training process to incorporate side information. In conclusion, GEase-K demonstrates resilience in highly sparse data and cold-start situations by combining linear and nonlinear structures and utilizing side information. Experimental results show that GEase-K outperforms GLocal-K based on the RMSE and MAE metrics on the highly sparse GoodReads and ModCloth datasets. Furthermore, in cold-start experiments divided into four groups using the GoodReads and ModCloth datasets, GEase-K denotes superior performance compared to GLocal-K.