• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.333-336
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• 2012

본 논문은 2.4GHz 대역을 사용하는 WBAN(Wireless Body Area Network)환경에서 동일 채널을 사용하는 경우에 발생하는 간섭을 제거하기 위해 MMSE(Minimum Mean Square Error), OC(Optimal Combining), ML(Maximum Likelihood)을 비교 분석하였으며, IEEE 802.15.6에서 정의된 시나리오 및 채널 모델에 대해서 분석하였다. 모의실험 결과 ML 알고리즘이 가장 우수한 성능을 나타내는 것을 알 수 있었다. 하지만 ML과 OC의 경우 원하는 신호의 채널 정보뿐만 아니라 간섭채널의 정보를 알아야 하기 때문에 채널을 추정하기 위한 훈련 심볼의 자리를 수신노드에서 알고 있어야 하므로 WBAN환경에 적용하는데 어려움이 있다. 따라서 복잡도와 성능사이에 적당한 trade-off를 만족하는 간섭 제거 알고리즘을 연구해야 할 것이다.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.157-167
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• 2020

With the recent advancement of computer hardware and the contribution of open source libraries to facilitate access to artificial intelligence technology, the use of machine learning (ML) and deep learning (DL) technologies in various fields of exploration geophysics has increased. In addition, ML researchers have developed complex algorithms to improve the inference accuracy of various tasks such as image, video, voice, and natural language processing, and now they are expanding their interests into the field of automatic machine learning (AutoML). AutoML can be divided into three areas: feature engineering, architecture search, and hyperparameter search. Among them, this paper focuses on hyperparamter search with Bayesian optimization, and applies it to the problem of facies classification using seismic data and well logs. The effectiveness of the Bayesian optimization technique has been demonstrated using Vincent field data by comparing with the results of the random search technique.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.5
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• pp.1-9
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• 1996

In this paper, we propose an efficient computational algorithm that can reduce significantly the computational complexity of the ML optimum multiuser detector known as the most excellent detector in synchronous code division multiple access channels. The proposed detector uses the sequential detection algorithm based on the alternating maximization appraoch to obtain the ML estimates. As initial estimates for this sequential algorithm, we can use the estimated values obtained by the conventional single-user detector, the linear decorrelating multiuser detector, or the decorrelating decision-feedback muliuser detector, the linear decorrelating multiuser detector, or the decorrelating decision-feedback multiuser detector. We have performed computer simulations in order to see the convergence behaviors and the detection performance of the propsoed algorithm in terms of initial algorithms and the number of users, and then to compare the computational complexity with that of the ML optimum multiuser detector. From the results, we have seen that the proposed alternating maximization detector has nearly equal detction performance with that of the ML optimum multiuser detctor in only a few iteration.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12C
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• pp.1082-1087
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• 2008

Maximum Likelihood (ML) detection is well known to exhibit better bit-error-rate (BER) than many other detectors for multiple-input multiple-output (MIMO) channel. However, ML detection has been shown a difficult problem due to its NP-hard problem. It means that there is no known algorithm which can find the optimal solution in polynomial-time. In this paper, Semi-Definite relaxation (SDR) is iteratively applied to ML detection problem. The probability distribution can be obtained by survival eigenvector out of the dominant eigenvalue term of the optimal solution. The probability distribution which is yielded by SDR is recurred to the received signal. Our approach can reach to nearly ML performance.

• 전자공학회논문지 IE
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• v.43 no.3
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• pp.13-21
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• 2006

Ryu et al. proposed a multiple target angle tracking algorithm using the angular measurement obtained from the signal subspace estimated by the output of sensor array. Ryu's algorithm has good features that it has no data association problem and simple structure. But its performance is seriously degraded in the low signal-to-noise ratio, and it uses the angular measurement obtained from the signal subspace of sampling time, even though the signal subspace is continuously updated by the output of sensor array. For improving the tracking performance of Ryu's algorithm, a measurement fusion method is derived based on ML(Maximum Likelihood) in this paper, and it admits us to use the angular measurements obtained form the adjacent signal subspaces as well as the signal subspace of sampling time. The new target angle tracking algorithm is proposed using the derived measurement fusion method. The proposed algorithm has a better tracking performance than that of Ryu's algorithm and it sustains the good features of Ryu's algorithm.

• Journal of Satellite, Information and Communications
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• v.5 no.1
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• pp.90-93
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• 2010

This paper shows various perfermance analysis utilizing STC(Space Time Coding) in MIMO mobile communication by WiBro system environments. In this paper, 3 algorithm which are SM method, ML (Maximum Likelihood) and ZF(Zero Forcing) algorithm use for perfermance analysis. From the various simulation result, it is confirm that ZF method is superior compare than STC and ML method.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06c
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• pp.166-171
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• 1994

기존의 음성인식기들은 일반적으로 간단하면서도 성능이 우수한 계층별 학습에 의해서 설계된다. 계층별 학습은 통계적 패턴인식에서의 ML 추정기법처럼 모델간의 독립성이 보장되고 무한한 양의 학습데이타가 주어진다는 가정에 기초하고 있다. 그러나, 대상어휘집합에 음운학적으로 유사한 어휘가 많이 포함되어 있는 인식문제에 있어서는 모델간의 독립성이 보장되지 못하고, 실제 주어지는 grktmqepdlk의 양도 제한되므로 기존의 합습알고리즘에는 한계가 있다. 따라서 본 논문에서는 그러한 가정상의 문제점으로 생기는 인식기의 성능저하를 개선할 수 있는 변별력 있는 학습알고리즘들을 검토하고 그의 일반적인 접근방법들에 대해서 논의한다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.18 no.11
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• pp.1237-1242
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• 2007

In this paper, we study STBC(Space Time Block Code) detection scheme in time varying Rayleigh fading channel. When the channel is varying during the time duration of STBC, the channel matrix of orthogonal STBC is not orthogonal. To get the optimum reception performance in this channel, joint ML detection scheme may be used, however this scheme requires high computation complexity. Decision feedback scheme is proposed to reduce the computation complexity with less reception performance. In this paper, we propose a novel STBC detection algorithm using double decision feedback which is less complex than the joint ML scheme and outperforms the conventional decision feedback scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1C
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• pp.110-116
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• 2008

Maximum likelihood(ML) diretion-of-arrival(DOA) estimation is essentially optimization of multivariable nonlinear cost function. Since the final estimate is highly dependent on the initial estimate, an initialization is critical in nonlinear optimization. We propose a multi-dimensional(M-D) search scheme of uniform exhaustive search and improved exhaustive search. Improved exhaustive search is superior to uniform exhaustive search in terms of the computational complexity and the accuracy of the estimates.

• The Korean Journal of Nuclear Medicine
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• v.34 no.4
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• pp.322-335
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• 2000

Purpose: For quantitative estimation of cerebrovascular reserve (CVR), we estimated the cerebral blood flow (CBF) using Lassen's nonlinearity correction algorithm and Tc-99m HMPAO brain SPECT images acquired with consecutive acquisition protocol. Using the values of CBF in basal and acetaBolamide (ACZ) stress states, CBF increase was calculated. Materials and Methods: In 9 normal subjects (age; $72{\pm}4$ years), brain SPECT was performed at basal and ACZ stress states consecutively after injection of 555 MBq and 1,110 MBq of Tc-99m HMPAO, respectively. Cerebellum was automatically extracted as reference region on basal SPECT image using threshold method. Assuming basal CBF of cerebellum as 55 ml/100g/min, CBF was calculated lot every pixel at basal states using Lassen's algorithm. Cerebellar blood flow at stress was estimated comparing counts of cerebellum at rest and ACZ stress and Lassen's algorithm. CBF of every pixel at ACZ stress state was calculated using Lassen's algorithm and ACZ cerebellar count. CVR was calculated by subtracting basal CBF from ACZ stress CBF for every pixel. The percent CVR was calculated by dividing CVR by basal CBF. The CBF and percentage CVR parametric images were generated. Results: The CBF and percentage CVR parametric images were obtained successfully in all the subjects. Global mean CBF were $49.6{\pm}5.5ml/100g/min\;and\;64.4{\pm}10.2ml/100g/min$ at basal and ACZ stress states, respectively. The increase of CBF at ACZ stress state was $14.7{\pm}9.6ml/100g/min$. The global mean percent CVR was 30.7% and was higher than the 13.8% calculated using count images. Conclusion: The blood flow at basal and ACZ stress states and cerebrovascular reserve were estimated using basal/ACZ Tc-99m-HMPAO SPECT images and Lassen's algorithm. Using these values, parametric images for blood flow and cerebrovascular reserve were generated.