• Title/Summary/Keyword: algorithms

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REPRESENTATION ALGORITHMS IN SOME FREE GROUPS

  • Choi, Su-Jeong
    • The Pure and Applied Mathematics
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    • v.15 no.3
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    • pp.229-243
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    • 2008
  • This paper is intended to clarify and verify two representation algorithms computing representations of elements of free groups generated by two linear fractional transformations. Moreover in practice some parts of the two algorithms are modified for computational efficiency. In particular the justification of the algorithms has been rigorously done by showing how both algorithms work correctly and efficiently according to inputs with some properties of the two linear fractional transformations.

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NEW ALGORITHMS FOR SOLVING ODES BY PSEUDOSPECTRAL METHOD

  • Darvishi, M.T.
    • Journal of applied mathematics & informatics
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    • v.7 no.2
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    • pp.439-451
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    • 2000
  • To compute derivatives using matrix vector multiplication method, new algorithms were introduced in [1.2]n By these algorithms, we reduced roundoff error in computing derivative using Chebyshev collocation methods (CCM). In this paper, some applications of these algorithms ar presented.

ALGORITHMS FOR MINIMAL FREE RESOLUTIONS HAVING MAXIMAL POSSIBLE BETTI NUMBERS

  • Shin, Yong-Su
    • Journal of applied mathematics & informatics
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    • v.15 no.1_2
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    • pp.393-404
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    • 2004
  • We introduce several algorithms for adding up Artinian O-sequences to obtain the maximal possible Betti numbers among all minimal free resolutions with the given Hilbert function. Moreover, we give open questions based on the outputs using those algorithms.

Poor Correlation Between the New Statistical and the Old Empirical Algorithms for DNA Microarray Analysis

  • Kim, Ju Han;Kuo, Winston P.;Kong, Sek-Won;Ohno-Machado, Lucila;Kohane, Isaac S.
    • Genomics & Informatics
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    • v.1 no.2
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    • pp.87-93
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    • 2003
  • DNA microarray is currently the most prominent tool for investigating large-scale gene expression data. Different algorithms for measuring gene expression levels from scanned images of microarray experiments may significantly impact the following steps of functional genomic analyses. $Affymetrix^{(R)}$ recently introduced high-density microarrays and new statistical algorithms in Microarray Suit (MAS) version 5.0$^{(R)}$. Very high correlations (0.92 - 0.97) between the new algorithms and the old algorithms (MAS 4.0) across several species and conditions were reported. We found that the column-wise array correlations had a tendency to be much higher than the row-wise gene correlations, which may be much more meaningful in the following higher-order data analyses including clustering and pattern analyses. In this paper, not only the detailed comparison of the two sets of algorithms is illustrated, but the impact of the introducing new algorithms on the further clustering analysis of microarray data and of possible pitfalls in mixing the old and the new algorithms were also described.

Iterative Algorithms for Interference Alignment in Cellular Network (셀룰러 네트워크상의 간섭정렬을 위한 반복 알고리즘)

  • Yeo, Jeong Ho;Cho, Joon Ho
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.37B no.10
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    • pp.947-955
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    • 2012
  • In this paper, we propose iterative algorithms to obtain the transmit and the receive vectors for interference alignment in cellular network. Although the conventional interference alignment algorithms for interference channels can be applied to cellular network, the number of iterations required to achieve a high sum rate is very large. The key idea in the proposed algorithms is to ignore intra-cell interference in updating the transmit vector for uplink and the receive vector for downlink. Numerical results show that the proposed algorithms achieve higher sum rates than the conventional algorithms for given iteration numbers when multiple antennas and a single carrier are used for interference alignment. It is also shown that the proposed algorithms outperform the conventional algorithms when a single antenna and multiple subcarriers are used for interference alignment.

Comparative Study on Static Task Scheduling Algorithms in Global Heterogeneous Environment (전역 이기종 환경에서의 정적 태스크 스케줄링의 비교 연구)

  • Kim Jung-Hwan
    • The KIPS Transactions:PartA
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    • v.13A no.2 s.99
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    • pp.163-170
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    • 2006
  • Most scheduling problems including DAG(Directed Acyclic Graph)-based are known to be NP-complete, so many heuristic-based scheduling algorithms have been researched. HEFT and CPOP are such algorithms which have been devised to be effective in heterogeneous environment. We proposed, in the previous research, three scheduling algorithms which are effective in realistic global heterogeneous environment: CPOC, eCPOPC and eCPOP. In this paper, the heuristics which are used in the above five algorithms will be systematically analyzed. Those algorithms will be also studied experimentally using various benchmarks. Experimental results show that the eCPOC generates better schedules than any other algorithms and the heuristics which are used in the proposed algorithms are effective in the global heterogeneous environment.

A Robustness Improvement of Adjoint-LMS Algorithms for Active Noise Control (능동소음제어를 위한 Adjoint-LMS 알고리즘의 강인성 개선)

  • Moon, Hak-ryong;Shon, Jin-geun
    • The Transactions of the Korean Institute of Electrical Engineers P
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    • v.65 no.3
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    • pp.171-177
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    • 2016
  • Noise problem that occurs in living environment is a big trouble in the economic, social and environmental aspects. In this paper, the filtered-X LMS algorithms, the adjoint LMS algorithms, and the robust adjoint LMS algorithms will be introduced for applications in active noise control(ANC). The filtered-X LMS algorithms is currently the most popular method for adapting a filter when the filter exits a transfer function in the error path. The adjoint LMS algorithms, that prefilter the error signals instead of divided reference signals in frequency band, is also used for adaptive filter algorithms to reduce the computational burden of multi-channel ANC systems such as the 3D space. To improve performance of the adjoint LMS ANC system, an off-line measured transfer function is connected parallel to the LMS filter. This parallel-fixed filter acts as a noise controller only when the LMS filter is abnormal condition. The superior performance of the proposed system was compared through simulation with the adjoint LMS ANC system when the adaptive filter is in normal and abnormal condition.

Improved Environment Recognition Algorithms for Autonomous Vehicle Control (자율주행 제어를 위한 향상된 주변환경 인식 알고리즘)

  • Bae, Inhwan;Kim, Yeounghoo;Kim, Taekyung;Oh, Minho;Ju, Hyunsu;Kim, Seulki;Shin, Gwanjun;Yoon, Sunjae;Lee, Chaejin;Lim, Yongseob;Choi, Gyeungho
    • Journal of Auto-vehicle Safety Association
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    • v.11 no.2
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    • pp.35-43
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    • 2019
  • This paper describes the improved environment recognition algorithms using some type of sensors like LiDAR and cameras. Additionally, integrated control algorithm for an autonomous vehicle is included. The integrated algorithm was based on C++ environment and supported the stability of the whole driving control algorithms. As to the improved vision algorithms, lane tracing and traffic sign recognition were mainly operated with three cameras. There are two algorithms developed for lane tracing, Improved Lane Tracing (ILT) and Histogram Extension (HIX). Two independent algorithms were combined into one algorithm - Enhanced Lane Tracing with Histogram Extension (ELIX). As for the enhanced traffic sign recognition algorithm, integrated Mutual Validation Procedure (MVP) by using three algorithms - Cascade, Reinforced DSIFT SVM and YOLO was developed. Comparing to the results for those, it is convincing that the precision of traffic sign recognition is substantially increased. With the LiDAR sensor, static and dynamic obstacle detection and obstacle avoidance algorithms were focused. Therefore, improved environment recognition algorithms, which are higher accuracy and faster processing speed than ones of the previous algorithms, were proposed. Moreover, by optimizing with integrated control algorithm, the memory issue of irregular system shutdown was prevented. Therefore, the maneuvering stability of the autonomous vehicle in severe environment were enhanced.

History of the Photon Beam Dose Calculation Algorithm in Radiation Treatment Planning System

  • Kim, Dong Wook;Park, Kwangwoo;Kim, Hojin;Kim, Jinsung
    • Progress in Medical Physics
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    • v.31 no.3
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    • pp.54-62
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    • 2020
  • Dose calculation algorithms play an important role in radiation therapy and are even the basis for optimizing treatment plans, an important feature in the development of complex treatment technologies such as intensity-modulated radiation therapy. We reviewed the past and current status of dose calculation algorithms used in the treatment planning system for radiation therapy. The radiation-calculating dose calculation algorithm can be broadly classified into three main groups based on the mechanisms used: (1) factor-based, (2) model-based, and (3) principle-based. Factor-based algorithms are a type of empirical dose calculation that interpolates or extrapolates the dose in some basic measurements. Model-based algorithms, represented by the pencil beam convolution, analytical anisotropic, and collapse cone convolution algorithms, use a simplified physical process by using a convolution equation that convolutes the primary photon energy fluence with a kernel. Model-based algorithms allowing side scattering when beams are transmitted to the heterogeneous media provide more precise dose calculation results than correction-based algorithms. Principle-based algorithms, represented by Monte Carlo dose calculations, simulate all real physical processes involving beam particles during transportation; therefore, dose calculations are accurate but time consuming. For approximately 70 years, through the development of dose calculation algorithms and computing technology, the accuracy of dose calculation seems close to our clinical needs. Next-generation dose calculation algorithms are expected to include biologically equivalent doses or biologically effective doses, and doctors expect to be able to use them to improve the quality of treatment in the near future.

A Comparison Study of Classification Algorithms in Data Mining

  • Lee, Seung-Joo;Jun, Sung-Rae
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.8 no.1
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    • pp.1-5
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    • 2008
  • Generally the analytical tools of data mining have two learning types which are supervised and unsupervised learning algorithms. Classification and prediction are main analysis tools for supervised learning. In this paper, we perform a comparison study of classification algorithms in data mining. We make comparative studies between popular classification algorithms which are LDA, QDA, kernel method, K-nearest neighbor, naive Bayesian, SVM, and CART. Also, we use almost all classification data sets of UCI machine learning repository for our experiments. According to our results, we are able to select proper algorithms for given classification data sets.