• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.10A
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• pp.969-979
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• 2002

This paper proposes an novel method of minimizing Interference which causes data decision error in digital wireless communications. In this method, in order to suppress ISI which is caused by the phase difference between the transmitted and received signal phases, the transmitted and received signals are always kept orthogonal by compensating the transmitted signal for detecting the phase noise and the delay of the received signal was implemented by MOS circuits. To delay the phase of the signal, additive white Gaussian noise (AWGN) environment was used. The phase and delay of the signal transmitted through AWGN channel were compensated in the modulator of the transmitter and the compensated signal was demodulated using quasi-direct conversion receiver and QPSK demodulator. ISI suppression was achieved by keeping the orthogonality between the compensated transmitted signal and the receive signal. The error probability of data decision was compared. By simulation the proposed system was proved to be effective in minimizing the ISI.

• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.506-511
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• 2018

This paper proposes an GMM(Gaussian Mixture Model)-DNN(Deep Neural Network) hybrid-based feature compensation method for effective speech recognition in noisy environments. In the proposed algorithm, the posterior probability for the conventional GMM-based feature compensation method is calculated using DNN. The experimental results using the Aurora 2.0 framework and database demonstrate that the proposed GMM-DNN hybrid-based feature compensation method shows more effective in Known and Unknown noisy environments compared to the GMM-based method. In particular, the experiments of the Unknown environments show 9.13 % of relative improvement in the average of WER (Word Error Rate) and considerable improvements in lower SNR (Signal to Noise Ratio) conditions such as 0 and 5 dB SNR.

• Aerospace Engineering and Technology
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• v.3 no.1
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• pp.179-187
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• 2004

We consider some problems of the Modified SPRT(Sequential Probability Ratio Test) method for fault detection and isolation of inertial redundant sensor systems and propose an Advanced SPRT method which solves the problems of the Modified SPRT method. The problems of the Modified SPRT method to apply to inertial sensor system come from the effect of inertial sensor errors and the correlation of parity vector components. We use a two-stage Kalman filter to remove effects of the inertial sensor errors and propose the modified parity vector and the controlled parity vector which reduces the effect of correlation of parity vector components. The Advanced SPRT method is derived form the modified parity vector and the controlled party vector. Some simulation results are presented to show the usefulness of the Advanced SPRT method to redundant inertial sensor systems.

• Fire Science and Engineering
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• v.31 no.2
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• pp.89-100
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• 2017

This study compared the activities satisfaction factors between volunteer firefighters and general volunteers in Sejong City. For this purpose, 112 volunteer firefighters and 227 general volunteers working in Sejong City were surveyed. In the questionnaire, the independent variable was the motivations, the interpersonal relationships, and the rewards and the dependent variable was the activities satisfaction. The reliability of the survey data in questionnaire design was analyzed by the SPSS 20.0 win program. The factor of the motivations, interpersonal relationships, and rewards had a significant positive influence on the activities satisfaction of volunteer firefighters and general volunteers (<0.05). The activities satisfaction of the volunteer firefighters was influenced in the order of the motivations, rewards, interpersonal relationships, and activities satisfaction of the general volunteers was influenced in order of the motivations, interpersonal relationships, and rewards.

• The Journal of the Korea Contents Association
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• v.7 no.12
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• pp.303-310
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• 2007

Compressed video bitstreams are intended for real-time transmission over communication networks. Because video compression algorithms eliminate the temporal, spatial, and statistical redundancies, the coded video bitstreams are very sensitive to transmission errors. We propose an error resilient video coding technique to limit the effect of error propagation in low bit-rate video coding. The success of error resilient coding techniques relies on how accurately the transmission errors can be detected. To detect the transmission error, we propose a very simple error detection technique based on data hiding Next, we conceal the corrupted MB data using intra MB refresh and motion compensation with the estimated motion vector and compare the simulation results. This method will be useful in video communication in error Prone environment such as WCDMA networks.

• Advanced Industrial SCIence
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• v.2 no.1
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• pp.21-38
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• 2023

An attempt has been made in this article to discuss the fundamentals of technical analysis of the stock market. A retail investor or trader may not have the wherewithal to source that kind of information. Technical analysis requires a candlestick chart only. Most of the brokers in India provide charting solutions as well. Studying the price action of a security or commodity or Forex generally indicates a price pattern. Prices react at certain levels and widely known as support and resistance levels. Since whatever is happening with the price of the security is considered to be a part of a pattern or cycle which has already played out sometime in the past, these studies help a keen technical analyst to identify with certain probability, the future movement of the price. Study of the candlestick patterns, price action, volumes and indicators offer the opportunities to identify a high probability trade with probable target and a stop loss. A trader or investor can take high probability trade or position and control only her losses.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.319-326
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• 2015

Recently in the fields o f stochastic optimal control ( SOC) and reinforcemnet l earning (RL), there have been a great deal of research efforts for the problem of finding data-based sub-optimal control policies. The conventional theory for finding optimal controllers via the value-function-based dynamic programming was established for solving the stochastic optimal control problems with solid theoretical background. However, they can be successfully applied only to extremely simple cases. Hence, the data-based modern approach, which tries to find sub-optimal solutions utilizing relevant data such as the state-transition and reward signals instead of rigorous mathematical analyses, is particularly attractive to practical applications. In this paper, we consider a couple of methods combining the modern SOC strategies and approximate inference together with machine-learning-based data treatment methods. Also, we apply the resultant methods to a variety of application domains including financial engineering, and observe their performance.

• Korean Journal of Social Welfare
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• v.69 no.3
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• pp.193-212
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• 2017

This study is purposed to explain the characteristics of injured workers' labor market situation and to analyze the factors influencing labor market transition of those workers. Using the Worker's Compensation Insurance Panel Data ver.1~3 which was surveyed by the Korean Workers' Compensation & Welfare Service in 2013-2015, this study analyzed 1,668 injured worker cases. The study shows that workers who have experience job retention at least once are 36.8% of all, 51.5% of them have experienced re-employment, and 36.9% have done unemployment. One result of the longitudinal analysis is that socio-demographic factors including gender, age, education years, convalescence period, ability on job performance, company size, term of service, temporary employment, daily-workers status before job accident and job training were associated with return to pre-injury job. The other result is that statistically significant factors affecting the probability to be the unemployed are gender, age, levels of disability, convalescence, ability on job performance, term of service before job accident, job rehabilitation service utilization. These findings indicate that we need to develop efficient intervention programs for supporting return-to-work and labor market transition of injured workers.

• The Journal of the Acoustical Society of Korea
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• v.41 no.4
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• pp.435-445
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• 2022

In this study, the reverberation of a bistatic sonar operated in southeastern coast in the East Sea in July 2020 was analyzed. The reverberation sensor data were collected through an LFM sound source towed by a research vessel and a horizontal line array receiver 1 km to 5 km away from it. The reverberation sensor data was analyzed by various methods including geo-plot after signal processing. Through this, it was confirmed that the angle reflected from the sound source through the scatterer to the receiver has a dominant influence on the distribution of the reverberation sound, and the probability distribution characteristics of bistatic sonar reverberation varies for each beam. In addition, parametric factors of K distribution and Rayleigh distribution were estimated from the sample through moment method estimation. Using the Kolmogorov-Smirnov test at the confidence level of 0.05, the distribution probability of the data was analyzed. As a result, it could be observed that the reverberation follows a Rayleigh probability distribution, and it could be estimated that this was the effect of a low reverberation to noise ratio.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.27-30
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• 2000

본 논문은 HMM(Hidden Markov Model)을 이용하여 인식을 수행할 경우의 오류를 최소화 할 수 있는 후 처리 과정으로 신경망을 결합시켜 HMM 단독으로 사용하였을 때 보다 높은 인식률을 얻을 수 있는 HMM과 신경망의 하이브리드시스템을 제안한다. HMM을 이용하여 학습한 후 학습에 참여하지 않은 데이터를 인식하였을 때 오인식 데이터를 정인식으로 인식하도록 HMM의 출력으로 얻은 각 출력확률을 후 처리에 사용될 MLP(Multilayer Perceptrons)의 학습용으로 사용하여 MLP를 학습하여 HMM과 MLP을 결합한 하이브리드 모델을 만든다. 이와 같은 HMM과 신경망을 결합한 하이브리드 모델을 사용하여 단독 숫자음과 4연 숫자음 데이터에서 실험한 결과 HMM 단독으로 사용하였을 때 보다 각각 약 $4.5\%$, $1.3\%$의 인식률 향상이 있었다. 기존의 하이브리드 시스템이 갖는 많은 학습시간이 소요되는 문제점과 실시간 음성인식시스템을 구현할 때의 학습데이터의 부족으로 인한 인식률 저하를 해결할 수 있는 방법임을 확인할 수 있었다.