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16th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)

作者： Simic, Dragan Jackowski, Konrad Jankowski, Dariusz Simic, Svetlana Univ Novi Sad Fac Tech Sci Trg Dositeja Obradovica 6 Novi Sad 21000 Serbia Wroclaw Univ Technol Dept Syst & Comp Networks PL-50370 Wroclaw Poland Univ Novi Sad Fac Med Novi Sad 21000 Serbia

ISBN: (纸本)9783319248349;9783319248332

Clustering is the task of partitioning data objects into groups, so that the objects within a cluster are similar to one another and dissimilar to the objects in other clusters. The efficiency random algorithm for good k is used to estimate the optimal number of clusters. In this research two important clustering algorithms, namely centroid based k-means, and representative object based fuzzy c-means clustering algorithms are compared in the original real-world U.S. cotton textile and apparel imports data set. This data set is not analyzed very often, it is dictated by business, economics and politics environments and its behaviour is not well known. The analysis of several different real-world economies and industrial data sets of one country is possible to predict it's economic development.

关键词： Data clustering Number of clusters k-means algorithm Fuzzy c-means random algorithm

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A New Maximum Weight Submatrix Solver Tool for Identifying Mutated Driver Pathways in Cancer using Python 10

A New Maximum Weight Submatrix Solver Tool for Identifying M...

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10th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Kharroubi, Fouad El Kati, Yassine Wang, Shu-Lin El Jourmi, Mohammed Ouahmane, Hassan Chouaib Doukkali Univ Natl Sch Appl Sci ENSAJ Networks & Comp Sci LTI Lab Dept Telecommun El Jadida 24002 Morocco Hunan Univ Sch Comp Sci & Elect Engn Changsha 41182 Peoples R China

ISBN: (纸本)9781728137834

Cancer may be a title given to all kind of infections caused by hereditary changes, such as substantial changes in DNA. Cancers are caused by mutations-which occur during cell division- that may possibly be inherited, provoked by environmental determinants, or result from some random DNA replication errors which simply maybe considered as bad luck. Recently in bioinformatics, one of the most heated topics is to search about the separation between driver mutations that lead to cancer and passenger mutations, which are well noted to be neutral and do not have any part in this disease development. Indeed, to solve this problem, several approaches and techniques were used in the literature. The main aim of this work is to show a new computation tool called "The Maximum Weight Submatrix Solver (MWS Solver)" using Python language. In fact, we programmed three metaheuristics called: Genetic algorithm, Evolutionary Programming and random Optimization algorithm, as an approximate approach in order to maximize the weight and sample all the possible driver pathways that can be found. A comparison of these approximate methods with other previous exact and random methods has also been taken into consideration. The experimental tests demonstrate that the proposed algorithms implemented via MWS Solver may become a useful open source and free complementary instrument for finding cancer pathways.

关键词： Maximum Weight Submatrix Problem Driver Pathways Driver Mutation Genetic algorithm random algorithm Evolutionary Programming

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Tolerable area creation with genetic algorithm

Tolerable area creation with genetic algorithm

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International Conference on Modern Problems of Radio Engineering, Telecommunication and Computer Science

作者： Krischuk, Vladimir Artyushenko, Bogdan Shilo, Galina Zaporozhzhia Natl Tech Univ Radio Design Dept UA-69063 Zaporizhzhia Ukraine

In this paper random methods for designing tolerable area are developed. Genetic algorithm for maximal tolerable area synthesis is presented. Tolerable area representation with intervals, ellipsoids and zonotopes are ... 详细信息

ISBN: (纸本)9781424413218

关键词： tolerable area zonotope interval random algorithm genetic algorithm

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A Dynamic-decoupling Controller of Current for Permanent Magnet Synchronous Motor 29

A Dynamic-decoupling Controller of Current for Permanent Mag...

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第29届中国控制与决策会议

作者： Mingxing Zhao Haiyan Zhao Hong Chen Department of Control Science and Engineering Jilin University State Key Laboratory of Automotive Simulation and Control Jilin University

ISBN: (纸本)9781509046584

The problem of current coupling in permanent magnet synchronous motor(PMSM) will affect control performance of the motor. The id = 0 control in the field-oriented control(FOC) can realize the static decoupling of current and improve the steady-state performance of the system. But this method does not solve the dynamic coupling problem of current, which will reduce the dynamic performance of the motor. Therefore, aiming at the problem of current dynamic coupling in PMSM system, a dynamic-decoupling controller of current based on Lyapunov stability theory is proposed in this paper. In order to solve the problem of controller gain selection in engineering, a random algorithm is adopted to narrow the range of controller gain. Compared with PID controller, the proposed controller can greatly decrease the number of controller gain and reduce the burden of controller gain selection simultaneously. Finally, the validity of proposed controller is verified by the model of PMSM system established in MATLAB/Simulink.

关键词： Permanent Magnet Synchronous Motor Field-oriented Control Dynamic-decoupling Controller Lyapunov Stability random algorithm

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Motion Compensation Based Fast Moving Object Detection in Dynamic Background 1st

Motion Compensation Based Fast Moving Object Detection in Dy...

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1st Chinese Conference on Computer Vision (CCCV)

作者： Zhang, Wei Li, Chenglong Zheng, Aihua Tang, Jin Luo, Bin Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China

ISBN: (纸本)9783662485705;9783662485699

This paper investigates robust and fast moving object detection in dynamic background. A motion compensation based approach is proposed to maintain an online background model, then the moving objects are detected in a fast fashion. Specifically, the pixel-level background model is built for each pixel, and is represented by a set of pixel values drawn from its location and neighborhoods. Given the background models of previous frame, the edge-preserving optical flow algorithm is employed to estimate the motion of each pixel, followed by propagating their background models to the current frame. Each pixel can be classified as foreground or background pixel according to the compensated background model. Moreover, the compensated background model is updated online by a fast random algorithm to adapt the variation of background. Extensive experiments on collected challenging videos suggest that our method outperforms other state-of-the-art methods, and achieves 8 fps in efficiency.

关键词： Fast object detection random algorithm Dynamic background Motion compensation

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Evolutionary computation techniques for nonlinear programming problems

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International Transactions in Operational Research 1994年第2期1卷 223-240页

作者： Michalewicz, Zbigniew University of North Carolina United States

This paper presents several evolutionary computation techniques and discusses their applicability to nonlinear programming problems. On the basis of this presentation we discuss also a construction of a new hybrid optimization system, Genocop II, and present its experimental results on a few test cases (nonlinear programming problems). © 1994.

关键词： constrained optimization evolutionary computation genetic algorithm nonlinear programming optimization technique random algorithm

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Genetic algorithms for numerical optimization

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Statistics and Computing 1991年第2期1卷 75-91页

作者： Michalewicz, Zbigniew Janikow, Cezary Z. Department of Computer Science University of North Carolina Charlotte 28223 NC United States Department of Computer Science University of North Carolina Chapel Hill 27599 NC United States

Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian striving for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where no algorithm is known. However, such applications can encounter problems that sometimes delay, if not prevent, finding the optimal solutions with desired precision. In this paper we describe applications of GAs to numerical optimization, present three novel ways to handle such problems, and give some experimental results. © 1991 Chapman & Hall.

关键词： constraint handling convergence Genetic algorithm local tuning optimization technique random algorithm

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Generalized Self-profit Maximization in Attribute Networks 15th

Generalized Self-profit Maximization in Attribute Networks

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15th Annual International Conference on Combinatorial Optimization and Applications (COCOA)

作者： Du, Liman Yang, Wenguo Gao, Suixiang Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

ISBN: (纸本)9783030926816;9783030926809

Profit maximization, an extension of classical Influence Maximization, asks for a small set of early adopters to maximize the expected total profit generated by all the adopters. This is a meaningful optimization problem in social network which has attracted many researchers' attention. Nevertheless, most of the related works are based on pure networks and one-entity diffusion model without considering the relationship of different promotional products and the influence of both emotion tendency and interest classification (label) in real marketing process. In this paper, we propose a novel nonsubmodular optimization problem, Generalized Self-profit Maximization in Attribute networks (GSMA), which is based on a community structure in attribute networks and captures the unilateral complementary relationship, a setting with complementary entities. To solve GSMA, the R-GSMA algorithm which is inspired by sampling method and martingale analysis is designed. We evaluate our proposed algorithm by conducting experiments on randomly generated and real datasets and show that R-GSMA is superior in effectiveness and accuracy comparing with other baseline algorithms.

关键词： Profit maximization Nonsubmodularity Attribute networks random algorithm Martingale

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COMPRESSED LEARNING OF HIGH-DIMENSIONAL SPARSE FUNCTIONS

COMPRESSED LEARNING OF HIGH-DIMENSIONAL SPARSE FUNCTIONS

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Schnass, Karin Vybiral, Jan Austrian Acad Sci Johann Radon Inst Computat & Appl Math A-4040 Linz Austria

ISBN: (纸本)9781457705397

This paper presents a simple randomised algorithm for recovering high-dimensional sparse functions, i.e. functions f : [0, 1](d) -> R which depend effectively only on k out of d variables, meaning f(x(1), ... , x(d)) = g(x(i1), ... , x(ik)), where the indices 1 <= i(1) < i(2) < ... < i(k) <= d are unknown. It is shown that (under certain conditions on g) this algorithm recovers the k unknown coordinates with probability at least 1-6 exp(-L) using only O(k(L+log k)(L+log d)) samples of f.

关键词： High dimensional function approximation random algorithm Hoeffding's inequality concentration of measure

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An Efficiency K-Means Data Clustering in Cotton Textile Imports 9th

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9th International Conference on Computer Recognition Systems (CORES)

作者： Simic, Dragan Svircevic, Vasa Sremac, Sinisa Ilin, Vladimir Simic, Svetlana Univ Novi Sad Fac Tech Sci Trg Dositeja Obradovica 6 Novi Sad 21000 Serbia Lames Ltd Jaracki Put Bb Sremska Mitrovica 22000 Serbia Univ Novi Sad Fac Med Hajduk Veljkova 1-9 Novi Sad 21000 Serbia

ISBN: (纸本)9783319262277;9783319262253

Data clustering is a technique of finding similar characteristics among the data sets which are always hidden in nature, and dividing them into groups. The major factor influencing cluster validation is choosing the optimal number of clusters. A novel random algorithm for estimating the optimal number of clusters is introduced here. The efficiency hybrid random algorithm for good k and modified classical k-means data clustering method in cotton textile imports country clustering and ranking is described and implemented on real-world data set. The original real-world U.S. cotton textile and apparel imports data set is taken under view in this research.

关键词： Data clustering Cluster k-means algorithm random algorithm

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