• Journal of Communications and Networks
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• 제16권4호
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• pp.436-446
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• 2014

The high intensity of research and modeling in fields of mathematics, physics, biology and chemistry requires new computing resources. For the big computational complexity of such tasks computing time is large and costly. The most efficient way to increase efficiency is to adopt parallel principles. Purpose of this paper is to present the issue of parallel computing with emphasis on the analysis of parallel systems, the impact of communication delays on their efficiency and on overall execution time. Paper focuses is on finite algorithms for solving systems of linear equations, namely the matrix manipulation (Gauss elimination method, GEM). Algorithms are designed for architectures with shared memory (open multiprocessing, openMP), distributed-memory (message passing interface, MPI) and for their combination (MPI + openMP). The properties of the algorithms were analytically determined and they were experimentally verified. The conclusions are drawn for theory and practice.

• 대한전자공학회논문지
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• 제25권12호
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• pp.1522-1531
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• 1988

The purpose of this paper is to explain eigenstructure based spatial spectrum estimation algorithms computing better estimates than the other approaches. Also, as an approach to overcome performance degradations in a multipath environments, the notion of forward and backwark spatial smoothing is discussed. Intensive simulation results,which include the comparisons of the eigenbased spatial spectral estimation algorithms in the situations of faulty estimation of the number of signals, are presented. The simulation results have shown that overestimation of the number of signals is more desirable than underestimation in using EV (Eigen Vector) and MUSIC (Multiple Signal Classification) algorithms and that underestimation of the number of signals is better strategy than overestimation in using eigenstructure based LP(Linear Prediction) algorithms.

• 한국통신학회논문지
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• 제27권8A호
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• pp.753-762
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• 2002

Blind equalization of transmission channel is important in communication areas and signal processing applications because it does not need training sequences, nor dose it require a priori channel information. In this paper, an adaptive blind MMSE channel equalization technique based on second-order statistics in investigated. We present an adaptive blind MMSE channel equalization using multichannel linear prediction error method for estimating cross-correlation vector. They can be implemented as RLS or LMS algorithms to recursively update the cross-correlation vector. Once cross-correlation vector is available, it can be used for MMSE channel equalization. Unlike many known subspace methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch. Performance of our algorithms and comparisons with existing algorithms are shown for real measured digital microwave channel.

• Nuclear Engineering and Technology
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• 제54권4호
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• pp.1195-1205
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• 2022

Recent developments on spectral diffusion algorithms, i.e., algorithms which exploit the projection of the solution on the eigenfunctions of the Laplacian operator, demonstrated their effective applicability in fast transient conditions. Nevertheless, the numerical error introduced by these algorithms, together with the uncertainties associated with model parameters, may impact the reliability of the predictions on short-lived volatile fission product release from nuclear fuel. In this work, we provide an upper bound on the numerical error introduced by the presented spectral diffusion algorithm, in both constant and time-varying conditions, depending on the number of modes and on the time discretization. The definition of this upper bound allows introducing a methodology to a priori bound the numerical error on short-lived volatile fission product retention.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제10권3호
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• pp.71-80
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• 2021

기계학습에서 정확한 데이터 예측을 위해서는 적절한 인공신경망 알고리즘을 선택해야 한다. 이러한 알고리즘에는 심층 신경망 (DNN), 반복 신경망 (RNN), 장단기 기억 (LSTM) 네트워크 및 게이트 반복 단위 (GRU) 신경망등을 들 수 있다. 개발자가 실험을 위해, 하나를 선택해야 하는 경우, 각 알고리즘의 성능에 대한 충분한 정보가 없었기 때문에, 직관에 의존할 수 밖에 없었다. 본 연구에서는 이러한 어려움을 완화하기 위해 실험을 통해 예측 오류(RMSE)와 처리 시간을 비교 평가 하였다. 각 알고리즘은 텐서플로우를 이용하여 구현하였으며, 세금 데이터를 사용하여 학습을 수행 하였다. 학습 된 모델을 사용하여, 세금 예측을 수행 하였으며, 실제값과의 비교를 통해 정확도를 측정 하였다. 또한, 활성화 함수와 다양한 최적화 함수들이 알고리즘에 미치는 영향을 비교 분석 하였다. 실험 결과, GRU 및 LSTM 알고리즘의 경우, RMSE(Root Mean Sqaure Error)는 0.12이고 R2값은 각각 0.78 및 0.75로 다른 알고리즘에 비해 더 낳은 성능을 보여 주었다. 기본 심층 신경망(DNN)의 경우, 처리 시간은 가장 낮지만 예측 오류는 0.163로 성능은 가장 낮게 측정 되었다. 최적화 알고리즘의 경우, 아담(Adam)이 오류 측면에서 최고의 성능을, 처리 시간 측면에서 최악의 성능을 보여 주었다. 본 연구의 연구결과는 데이터 예측을 위한 알고리즘 선택시, 개발자들에게 유용한 정보로 사용될 것으로 예상된다.

• 지능정보연구
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• 제16권4호
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• pp.99-112
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• 2010

앙상블 학습은 분류 및 예측 알고리즘의 성과개선을 위하여 제안된 기계학습 기법이다. 그러나 앙상블 학습은 기저 분류자의 다양성이 부족한 경우 다중공선성 문제로 인하여 성과개선 효과가 미약하고 심지어는 성과가 악화될 수 있다는 문제점이 제기되었다. 본 연구에서는 기저 분류자의 다양성을 확보하고 앙상블 학습의 성과개선 효과를 제고하기 위하여 유전자 알고리즘 기반의 범위 최적화 기법을 제안하고자 한다. 본 연구에서 제안된 최적화 기법을 기업 부실예측 인공신경망 앙상블에 적용한 결과 기저 분류자의 다양성이 확보되고 인공신경망 앙상블의 성과가 유의적으로 개선되었음을 보여주었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권9호
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• pp.4087-4107
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• 2016

The current cloud computing paradigm is still vulnerable to a significant number of system failures. The increasing demand for fault tolerance and resilience in a cost-effective and device-independent manner is a primary reason for creating an effective means to address system dependability and availability concerns. This paper focuses on online failure prediction for cloud computing systems using system runtime data, which is different from traditional tolerance techniques that require an in-depth knowledge of underlying mechanisms. A 'failure prediction' approach, based on Cloud Theory (CT) and the Hidden Markov Model (HMM), is proposed that extends the HMM by training with CT. In the approach, the parameter ω is defined as the correlations between various indices and failures, taking into account multiple runtime indices in cloud computing systems. Furthermore, the approach uses multiple dimensions to describe failure prediction in detail by extending parameters of the HMM. The likelihood and membership degree computing algorithms in the CT are used, instead of traditional algorithms in HMM, to reduce computing overhead in the model training phase. Finally, the results from simulations show that the proposed approach provides very accurate results at low computational cost. It can obtain an optimal tradeoff between 'failure prediction' performance and computing overhead.

• 농업과학연구
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• 제46권1호
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• pp.67-78
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• 2019

The purpose of this study was to predict the water quality using the RNN (recurrent neutral network) and LSTM (long short-term memory). These are advanced forms of machine learning algorithms that are better suited for time series learning compared to artificial neural networks; however, they have not been investigated before for water quality prediction. Three water quality indexes, the BOD (biochemical oxygen demand), COD (chemical oxygen demand), and SS (suspended solids) are predicted by the RNN and LSTM. TensorFlow, an open source library developed by Google, was used to implement the machine learning algorithm. The Okcheon observation point in the Geum River basin in the Republic of Korea was selected as the target point for the prediction of the water quality. Ten years of daily observed meteorological (daily temperature and daily wind speed) and hydrological (water level and flow discharge) data were used as the inputs, and irregularly observed water quality (BOD, COD, and SS) data were used as the learning materials. The irregularly observed water quality data were converted into daily data with the linear interpolation method. The water quality after one day was predicted by the machine learning algorithm, and it was found that a water quality prediction is possible with high accuracy compared to existing physical modeling results in the prediction of the BOD, COD, and SS, which are very non-linear. The sequence length and iteration were changed to compare the performances of the algorithms.

• Journal of the Korean Data and Information Science Society
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• 제18권2호
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• pp.489-506
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• 2007

Gene structure prediction, which is to predict protein coding regions in a given nucleotide sequence, is the most important process in annotating genes and greatly affects gene analysis and genome annotation. As eukaryotic genes have more complicated structures in DNA sequences than those of prokaryotic genes, analysis programs for eukaryotic gene structure prediction have more diverse and more complicated computational models. There are Ab Initio method, Similarity-based method, and Ensemble method for gene prediction method for eukaryotic genes. Each Method use various algorithms. This paper introduce how to predict genes using HMM(Hidden Markov Model) algorithm and present the process of gene prediction with well-known gene prediction programs.

• Journal of Microbiology
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• 제44권2호
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• pp.137-144
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• 2006

The problems associated with gene identification and the prediction of gene structure in DNA sequences have been the focus of increased attention over the past few years with the recent acquisition by large-scale sequencing projects of an immense amount of genome data. A variety of prediction programs have been developed in order to address these problems. This paper presents a review of the computational approaches and gene-finders used commonly for gene prediction in eukaryotic genomes. Two approaches, in general, have been adopted for this purpose: similarity-based and ab initio techniques. The information gleaned from these methods is then combined via a variety of algorithms, including Dynamic Programming (DP) or the Hidden Markov Model (HMM), and then used for gene prediction from the genomic sequences.