• 한국컴퓨터정보학회논문지
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• 제11권4호
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• pp.69-75
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• 2006

과학 기술이 발전함에 따라 대량의 정보를 처리하기 위해 대두된 인터넷을 기반으로 하는 분산 컴퓨팅 환경은 대규모의 독립된 자원을 공유하여 과학 연구와 같은 문제를 해결하기 위한 구축된 환경이므로, 사용자 작업을 효율적으로 할당하기 위한 스케줄링 알고리즘이 필요하다. 현재까지 여러 스케줄링 알고리즘이 연구되어 왔지만, 대부분 피어의 자율성을 고려하지 않는 문제점을 가지고 있다. 본 논문에서는 이러한 문제점을 해결하기 위하여 인터넷기반 분산 컴퓨팅 환경에서의 피어 가용길이 예상 기법을 제안하였다. 또한 인터넷기반 분산 컴퓨팅환경에서 사용되는 SRTFIT 알고리즘에 적용하여, 시뮬레이션을 통하여 제안된 기법이 단순한 예상기법보다 성능이 뛰어남을 보였다.

• Journal of the Optical Society of Korea
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• 제17권6호
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• pp.525-532
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• 2013

Contrary to the academic interests of other existing studies elsewhere, this study deals with how the alignment algorithms such as sensitivity or Differential Wavefront Sampling (DWS) can be better used under effects from field, compensator positioning and environmental errors unavoidable from the shop-floor alignment work. First, the influences of aforementioned errors to the alignment state estimation was investigated with the algorithms. The environmental error was then found to be the dominant factor influencing the alignment state prediction accuracy. Having understood such relationship between the distorted system wavefront caused by the error sources and the alignment state prediction, we used it for simulated and experimental alignment runs for Infrared Optical System (IROS). The difference between trial alignment runs and experiment was quite close, independent of alignment methods; 6 nm rms for sensitivity method and 13 nm rms for DWS. This demonstrates the practical usefulness and importance of the prior error analysis using the alignment algorithms before the actual alignment runs begin. The error analysis methodology, its application to the actual alignment of IROS and their results are described together with their implications.

• Advances in concrete construction
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• 제13권1호
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• pp.11-23
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• 2022

Conventional concrete needs some improvement in the mechanical properties, which can be obtained by different admixtures. However, making concrete samples costume always time and money. In this paper, different types of hybrid algorithms are applied to develop predictive models for forecasting compressive strength (CS) of concretes containing metakaolin (MK) and fly ash (FA). In this regard, three different algorithms have been used, namely multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVR), to predict CS of concretes by considering most influencers input variables. These algorithms integrated with the grey wolf optimization (GWO) algorithm to increase the model's accuracy in predicting (GWMLP, GWRBF, and GWSVR). The proposed MLP models were implemented and evaluated in three different layers, wherein each layer, GWO, fitted the best neuron number of the hidden layer. Correspondingly, the key parameters of the SVR model are identified using the GWO method. Also, the optimization algorithm determines the hidden neurons' number and the spread value to set the RBF structure. The results show that the developed models all provide accurate predictions of the CS of concrete incorporating MK and FA with R² larger than 0.9972 and 0.9976 in the learning and testing stage, respectively. Regarding GWMLP models, the GWMLP1 model outperforms other GWMLP networks. All in all, GWSVR has the worst performance with the lowest indices, while the highest score belongs to GWRBF.

• 한국공간구조학회논문집
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• 제23권4호
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• 융합신호처리학회논문지
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• 제21권2호
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• pp.55-60
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• 2020

본 논문에서는 우선순위와 문턱치를 가지고 최적 후보의 조기 탐지를 이용한 움직임 추정의 고속 블록 매칭 알고리즘을 제안한다. 전 영역 탐색(full search) 알고리즘의 계산량을 줄이기 위해 많은 고속 움직임 추정 알고리즘이 발표되었지만, 여전히 움직임 추정 성능을 향상시키기 위한 많은 연구가 보고되고 있다. 제안된 알고리즘은 이전 부분 매칭 오류에서 우선순위가 높은 각 후보에 대한 블록 매칭 오류를 계산한다. 제안된 알고리즘은 대부분의 기존 고속 블록 매칭 알고리즘에 추가적으로 적용하여 속도를 높일 수 있다. 그렇게 함으로써 최소 오류 지점을 조기에 찾고 불가능한 후보에 대한 불필요한 계산을 줄임으로써 속도를 높일 수 있다. 제안된 알고리즘은 전 영역 탐색 알고리즘과 동일한 예측 화질을 가지면서 기존의 고속 무손실 탐색 알고리즘보다 적은 계산을 사용한다. 실험결과로서, 제안된 알고리즘은 예측 화질 저하 없이 PDE 및 전 영역 탐색 방법의 계산에 비해 30 ~ 70%까지 줄일 수 있으며, 다른 고속 손실 알고리즘을 사용하면 더욱 감소시키는 것으로 나타났다.

• 한국군사과학기술학회지
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• 제16권2호
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• pp.185-190
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• 2013

The previous steganographic methods by using the interpolation were difficult to estimate the distortion because the size of cover image is extended by interpolation algorithms. In this paper, to solve the problems of previous methods proposed the improved steganographic method based on the pixel replacement algorithms. In our method, we cannot extend a cover image, but also can estimate exactly the distortion of the stego-images. In the experimental results, the estimated distortion and embedding capacity of stego-image are shown on three pixel replacement methods.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2006년도 춘계학술발표회 논문집(I)
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• pp.494-497
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• 2006

The objectives of this study are to develop and evaluate the Neural Network algorithm which can predict the inelastic shortening such as the creep strain and the drying shrinkage strain of reinforced concrete members using the previous test data. New learning algorithms for the prediction of creep strain and the drying shrinkage strain are proposed focusing on input layer components and a normalization method for input data and their validity is examined through several test data. In Neural Network algorithm, the main input data to be trained are the compressive strength of the concrete, volume to surface ratio, curing condition, relative humidity, and the applied load. The results show that the new algorithms proposed herein successfully predict creep strain and the drying shrinkage strain.

• 한국정보통신학회논문지
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• 제1권1호
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• pp.77-83
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• 1997

본 논문에서는 현재 디지털 이동통신 시스템에서 운용되고 있는 QCELP(Qualcomm Code Excited Linear Predictor) 음성부호화 방식과 향후 IMT-2000 (International Mobile Telecommunications 2000) 등의 시스템에서 사용 예정인 CS-ACELP(Conjugate Structure Algebraic Code Excited Linear Prediction) 음성부호화 방식과의 성능을 비교한다. 특히 무선 채널을 사용하는 이동통신환경의 특징인 채널에러로 인한 음성부호화기의 성능을 비교함으로써 채널에러에 강인한 음성부호화 알고리즘 설계에 대한 고찰을 유도한다.

• Bioinformatics and Biosystems
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• 제2권2호
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• pp.52-57
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• 2007

The computational discovery of transcription factor binding site is one of the important tools in the genetic and genomic analysis. Rough prediction of gene regulation network and finding possible co-regulated genes are typical applications of the technique. Countless motif-discovery algorithms have been proposed for the past years. However, there is no dominant algorithm yet. Each algorithm does not give enough accuracy without extensive information. In this paper, we explore the possibility of combining multiple algorithms for the one integrated result in order to improve the performance and the convenience of researchers. Moreover, we apply new high order information that is reorganized from the set of basis predictions to the final prediction.