• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.326-326
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• 2021

장기간에 걸쳐 넓은 지역에 대해 발생하는 가뭄을 예측하기위해 많은 학자들의 기술적, 학술적 시도가 있어왔다. 본 연구에서는 복잡한 시계열을 가진 가뭄을 전망하는 방법 중 시나리오에 기반을 둔 가뭄전망 방법과 실시간으로 가뭄을 예측하는 비시나리오 기반의 방법 등을 이용하여 미래 가뭄전망을 실시했다. 시나리오에 기반을 둔 가뭄전망 방법으로는, 3개월 GCM(General Circulation Model) 예측 결과를 바탕으로 2009년도 PDSI(Palmer Drought Severity Index) 가뭄지수를 산정하여 가뭄심도에 대한 단기예측을 실시하였다. 또, 통계학적 방법과 물리적 모델(Physical model)에 기반을 둔 확정론적 수치해석 방법을 이용하여 비시나리오 기반 가뭄을 예측했다. 기존 가뭄을 통계학적 방법으로 예측하기 위해서 시도된 대표적인 방법으로 ARIMA(Autoregressive Integrated Moving Average) 모델의 예측에 대한 한계를 극복하기위해 서포트 벡터 회귀(support vector regression, SVR)와 웨이블릿(wavelet neural network) 신경망을 이용해 SPI를 측정하였다. 최적모델구조는 RMSE(root mean square error), MAE(mean absolute error) 및 R(correlation Coefficient)를 통해 선정하였고, 1-6개월의 선행예보 시간을 갖고 가뭄을 전망하였다. 그리고 SPI를 이용하여, 마코프 연쇄(Markov chain) 및 대수선형모델(log-linear model)을 적용하여 SPI기반 가뭄예측의 정확도를 검증하였으며, 터키의 아나톨리아(Anatolia) 지역을 대상으로 뉴로퍼지모델(Neuro-Fuzzy)을 적용하여 1964-2006년 기간의 월평균 강수량과 SPI를 바탕으로 가뭄을 예측하였다. 가뭄 빈도와 패턴이 불규칙적으로 변하며 지역별 강수량의 양극화가 심화됨에 따라 가뭄예측의 정확도를 높여야 하는 요구가 커지고 있다. 본 연구에서는 복잡하고 비선형성으로 이루어진 가뭄 패턴을 기상학적 가뭄의 정도를 나타내는 표준강수증발지수(SPEI, Standardized Precipitation Evapotranspiration Index)인 월SPEI와 일SPEI를 기계학습모델에 적용하여 예측개선 모형을 개발하고자 한다.

• Convergence Security Journal
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• v.23 no.2
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• pp.37-45
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• 2023

Cyber deception technology plays a crucial role in monitoring attacker activities and detecting new types of attacks. However, along with the advancements in deception technology, the development of Anti-honeypot technology has allowed attackers who recognize the deceptive environment to either cease their activities or exploit the environment in reverse. Currently, deception technology is unable to identify or respond to such situations. In this study, we propose a predictive model using Markov chain analysis to determine the identification of attackers who infiltrate deceptive environments. The proposed model for deception status determination is the first attempt of its kind and is expected to overcome the limitations of existing deception-based attacker analysis, which does not consider attackers who identify the deceptive environment. The classification model proposed in this study demonstrated a high accuracy rate of 97.5% in identifying and categorizing attackers operating in deceptive environments. By predicting the identification of an attacker's deceptive environment, it is anticipated that this model can provide refined data for numerous studies analyzing deceptive environment intrusions.

• Economic Analysis
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• v.17 no.2
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• pp.27-55
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• 2011

Although many theoretical studies have tried to explain the volatility in financial markets using models of herd behavior, there have been few empirical studies on dynamic herding due to the technical difficulty of detecting herd behavior with time-series data. Thus, this paper theoretically extends a continuous beliefs system belonging to an agent based economic model by introducing a term representing agents'mutual dependence into each agent's utility function and derives a SV(stochastic volatility)-type econometric model. From this model the time-varying herding parameters are efficiently estimated by a Markov chain Monte Carlo method. Using monthly data of KOSPI and DOW, this paper provides some empirical evidences for stronger herding in the Korean stock market than in the U.S. stock market, and further stronger herding after the global financial crisis than before it. More interesting finding is that time-varying herd behavior has weak autocorrelation and the global financial crisis may increase its volatility significantly.

• The Journal of the Acoustical Society of Korea
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• v.26 no.6
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• pp.259-268
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• 2007

This paper contributes to an improvement of the statistical bandwidth extension(BWE) system based on Hidden Markov Model(HMM). First, the existing HMM training method for BWE, which is suggested originally by Jax, is analyzed in comparison with the general Baum-Welch training method. Next, based on this analysis, a new HMM-based BWE method is suggested which adopts the Baum-Welch re-estimation algorithm instead of the Jax's to train HMM model. Conclusionally speaking, the Baum-Welch re-estimation algorithm is a generalized form of the Jax's training method. It is flexible and adaptive in modeling the statistical characteristic of training data. Therefore, it generates a better model to the training data, which results in an enhanced BWE system. According to experimental results, the new method performs much better than the Jax's BWE systemin all cases. Under the given test conditions, the RMS log spectral distortion(LSD) scores were improved ranged from 0.31dB to 0.8dB, and 0.52dB in average.

• Journal of Intelligence and Information Systems
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• v.17 no.3
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• pp.187-201
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• 2011

KOSPI200 index is the Korean stock price index consisting of actively traded 200 stocks in the Korean stock market. Its base value of 100 was set on January 3, 1990. The Korea Exchange (KRX) developed derivatives markets on the KOSPI200 index. KOSPI200 index futures market, introduced in 1996, has become one of the most actively traded indexes markets in the world. Traders can make profit by entering a long position on the KOSPI200 index futures contract if the KOSPI200 index will rise in the future. Likewise, they can make profit by entering a short position if the KOSPI200 index will decline in the future. Basically, KOSPI200 index futures trading is a short-term zero-sum game and therefore most futures traders are using technical indicators. Advanced traders make stable profits by using system trading technique, also known as algorithm trading. Algorithm trading uses computer programs for receiving real-time stock market data, analyzing stock price movements with various technical indicators and automatically entering trading orders such as timing, price or quantity of the order without any human intervention. Recent studies have shown the usefulness of artificial intelligent systems in forecasting stock prices or investment risk. KOSPI200 index data is numerical time-series data which is a sequence of data points measured at successive uniform time intervals such as minute, day, week or month. KOSPI200 index futures traders use technical analysis to find out some patterns on the time-series chart. Although there are many technical indicators, their results indicate the market states among bull, bear and flat. Most strategies based on technical analysis are divided into trend following strategy and non-trend following strategy. Both strategies decide the market states based on the patterns of the KOSPI200 index time-series data. This goes well with Markov model (MM). Everybody knows that the next price is upper or lower than the last price or similar to the last price, and knows that the next price is influenced by the last price. However, nobody knows the exact status of the next price whether it goes up or down or flat. So, hidden Markov model (HMM) is better fitted than MM. HMM is divided into discrete HMM (DHMM) and continuous HMM (CHMM). The only difference between DHMM and CHMM is in their representation of state probabilities. DHMM uses discrete probability density function and CHMM uses continuous probability density function such as Gaussian Mixture Model. KOSPI200 index values are real number and these follow a continuous probability density function, so CHMM is proper than DHMM for the KOSPI200 index. In this paper, we present an artificial intelligent trading system based on CHMM for the KOSPI200 index futures system traders. Traders have experienced on technical trading for the KOSPI200 index futures market ever since the introduction of the KOSPI200 index futures market. They have applied many strategies to make profit in trading the KOSPI200 index futures. Some strategies are based on technical indicators such as moving averages or stochastics, and others are based on candlestick patterns such as three outside up, three outside down, harami or doji star. We show a trading system of moving average cross strategy based on CHMM, and we compare it to a traditional algorithmic trading system. We set the parameter values of moving averages at common values used by market practitioners. Empirical results are presented to compare the simulation performance with the traditional algorithmic trading system using long-term daily KOSPI200 index data of more than 20 years. Our suggested trading system shows higher trading performance than naive system trading.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.12
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• pp.1919-1927
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• 1993

A new MAC protocol has been proposed and analysed to relieve the unfairness problems exhibited by the basic version of the DQDB standard. DQDB MAC protocol has the unfairness problems in throughputs. message delay and so or. And when the slots are reused or the file transmissions takes long, the unfairness problems in the system become worse. The new access protocol proposed here, which of called the Balanced DQDB, guarantees a fair bandwidth distribution by using one bit of the dual bus network protocol and keeps up all characteristics of DQDB. the DQDB analysis model introduced by Wen Jing, et al, was considered to analyse a sequential balance distribution of solts. And the probabilities of the empty in operation mode were represented to determine the probabilities for busy bits to generate on each node of the bus using the Markov chain. Through the simulations. the performances of the proposed Balanced DQDB and that of the standard DQDB of the BWB mechanism were compared at the state that the values of the RQ or CD counter on each node varied dynamically. As the results, it is shown that the Balanced DQDB has the decrement of throughputs in upstream, but the numbers of the used empty slots at each node of the Balanced DQDB had more than that of the others because the Balanced DQDB has over 0.9 throughputs in the 70~80% nodes of total node and it has constant throughputs at each node. And there results were analogous to that of the analytical model.

• Journal of KIISE:Information Networking
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• v.32 no.4
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• pp.515-524
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• 2005

As the number of the mobile nodes (MNs) increases in the networks, the signaling traffic generated by mobility management for MNs will increase explosively, and such a phenomenon will probably affect overall network performance. In this paper, we propose a novel analytical approach using a continuous-time Markov chain model and hierarchical network model for the analysis on the signaling load of representative IPv6 mobility support Protocols such as Mobile IPv6 (MIPv6) and Hierarchical Mobile IPv6 (HMIPv6). According to these analytical modeling, this paper derives the various signaling costs, which are generated by an MN during its average domain residence time when MIPv6 and HMIPv6 are deployed under the same network architecture, respectively. In addition, based on these derived costs, we investigate the effects of various mobility/traffic-related parameters on the signaling costs generated by an MN under MIPv6 and HMIPv6. The analytical results show that as the average moving speed of an MN gets higher and the binding lifetime is set . to the larger value, and as its average packet arrival rate gets lower, the total signaling cost generated during its average domain residence time under HMIPv6 will get relatively lower than that under MIPv6, and that under the reverse conditions, the total signaling cost under MIPv6 will get relatively lower than that under HMIPv6.

• The Journal of the Acoustical Society of Korea
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• v.21 no.6
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• pp.552-558
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• 2002

Recently, based on the human speech recognition (HSR) model of Fletcher, the multi-band speech recognition has been intensively studied by many researchers. As a new automatic speech recognition (ASR) technique, the multi-band speech recognition splits the frequency domain into several sub-bands and recognizes each sub-band independently. The likelihood scores of sub-bands are weighted according to reliabilities of sub-bands and re-combined to make a final decision. This approach is known to be robust under noisy environments. When the noise is stationary a sub-band SNR can be estimated using the noise information in non-speech interval. However, if the noise is non-stationary it is not feasible to obtain the sub-band SNR. This paper proposes the inverse sub-band distance (ISD) weighting, where a distance of each sub-band is calculated by a stochastic matching of input feature vectors and hidden Markov models. The inverse distance is used as a sub-band weight. Experiments on 1500∼1800㎐ band-limited white noise and classical guitar sound revealed that the proposed method could represent the sub-band reliability effectively and improve the performance under both stationary and non-stationary band-limited noise environments.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.9
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• pp.25-37
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• 2007

The existing route re-establishment methods which intend to extend the lifetime of the network attempt to find new routes in order not to overly consume energy of certain nodes. These methods outperform other routing algorithms in the network lifetime extension aspect because they try to consume energy evenly for the entire network. However, these algorithms involve heavy signaling overheads because they find new routes based on the flooding method and route re-acquisition occurs often. Because of the overhead they often can not achieve the level of performance they intend to. In this paper, we propose a new route re-acquisition algorithm ARROW which takes into account the cost involved in the packet transmission and the route re-acquisition. Since the proposed algorithm considers future route re-acquisition costs when it first finds the route, it spends less energy to transmit given amount of data while evenly consuming the energy as much as possible. Using 2-dimensional Markov Chain model, we compare the performance of the proposed algorithm and that of other algorithms. Analysis results show that the proposed algorithm outperforms the existing route re-acquisition methods in the signaling overhead and network lifetime aspects.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.18 no.2
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• pp.179-194
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• 2020

A Bayesian approach was introduced to improve the belief of prior distributions of input parameters for the probabilistic safety assessment of radioactive waste repository. A GoldSim-based module was developed using the Markov chain Monte Carlo algorithm and implemented through GSTSPA (GoldSim Total System Performance Assessment), a GoldSim template for generic/site-specific safety assessment of the radioactive repository system. In this study, sequential Bayesian updating of prior distributions was comprehensively explained and used as a basis to conduct a reliable safety assessment of the repository. The prior distribution to three sequential posterior distributions for several selected parameters associated with nuclide transport in the fractured rock medium was updated with assumed likelihood functions. The process was demonstrated through a probabilistic safety assessment of the conceptual repository for illustrative purposes. Through this study, it was shown that insufficient observed data could enhance the belief of prior distributions for input parameter values commonly available, which are usually uncertain. This is particularly applicable for nuclide behavior in and around the repository system, which typically exhibited a long time span and wide modeling domain.