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Cross-layer parallel cooperative spectrum sensing for heterogeneous channels based on iterative km algorithm

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第2期22卷 S2629-S2637页

作者： Fu, Shuang Zhang, Guoyin Shang, Tingyi Heilongjiang Bayi Agr Univ Inst Elect & Informat Daqing 163319 Peoples R China Harbin Engn Univ Inst Coll Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China

To address the problem of spectrum scarcity in future communication for smart world, cognitive radio (CR) is viewed as an effective way and has been widely studied in recent years. Spectrum sensing is the key to deploy Cognitive radio (CR) network. Parallel cooperative spectrum sensing provides an effective solution in multi-channel scenario. In this paper, joint optimal sensing duration selection and sensing task allocation is studied in parallel cooperative spectrum sensing for heterogeneous multi-channel CR systems. Two cross-layer parallel cooperative spectrum sensing methods based on iterative km algorithm are proposed. Sensing duration selection and sensing task allocation are designed jointly to maximize the available throughput. A two-step method is implemented. Firstly it determines the optimal sensing task allocation for fixed sensing duration by iterative km algorithm, and then selects the optimal sensing duration by exhaustion method and quartering method, respectively. The simulation results show that the proposed method can optimally select the sensing duration and obtain higher available throughput than other compared methods.

关键词： Cognitive radio Spectrum sensing km algorithm Sensing duration

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An Improved km algorithm for Computing the Structural Index of DAE System

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JOURNAL OF algorithmS & COMPUTATIONAL TECHNOLOGY 2015年第3期9卷 233-250页

作者： Zeng, Yan Wu, Xuesong Cao, Jianwen Chinese Acad Sci Inst Software Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing 100190 Peoples R China

Modeling and simulation technology is widely used to design complex products in industry. The problem of solving DAEs(Differential Algebraic Equations) is a key part of modeling and simulation technology, and computing the structural index of DAEs correctly and efficiently is very important to solve DAEs. The traditional algebraic method to compute the structural index is very costly. In this paper, we firstly convert the problem of computing the structural index of DAEs into the maximum weighted matching problem of bipartite graph, reducing a mass of symbolic manipulations;and then, we present an improved km algorithm(called as Greedy_km in this paper) based on the properties of DAEs to solve this matching problem. In order to solve the matching problem efficiently, it firstly computes matches as much as possible using greedy strategy, and then call km algorithm to search the matches for the unmatched vertices after the step of greedy strategy. This paper also gives a set of numerical experiments to evaluate the time performance of our method. The results show that the time performance of Greedy_km algorithm is significantly improved compared with the traditional Gaussian elimination algorithm and classical km algorithm.

关键词： DAE Index Reduction Structural Index Bipartite Graph Greedy km algorithm

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An Improved km algorithm for Computing Structural Index of DAE System

An Improved KM Algorithm for Computing Structural Index of D...

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12th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Zeng, Yan Wu, Xuesong Cao, Jianwen Chinese Acad Sci Inst Software Lab Parallel Software & Computat Sci Software Beijing 100190 Peoples R China

ISBN: (纸本)9780769550602

This paper proposes an improved km algorithm to computing the structural index of linear time-invariant Differential Algebraic Equation (DAE) systems. The problem is of practical significance in index reduction based on structural index of DAE system and combinatorial relaxation theory. This improved km algorithm combines greedy idea and classical km algorithm. It first computes matches as much as possible using greedy technology, and then call km algorithm to search the matches for the unmatched vertices during the step of greedy technology. The improved km algorithm reduces the running time bound by a factor of r, the number of matches searched using greedy algorithm. Generally, the time complexity is O(r(2) + (n - r)n(2)), the optimal time is O(n(2)).

关键词： DAE Index Reduction Structural Index Bipartite Graph Greedy km algorithm

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Non-Intrusive Load Identification Method Based on Improved km algorithm

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IEEE ACCESS 2019年 7卷 151368-151377页

作者： Xiao, Yong Hu, Yue He, Hengjing Zhou, Dongguo Zhao, Yun Hu, Wenshan Elect Power Res Inst Guangzhou 510663 Guangdong Peoples R China Wuhan Univ Sch Elect & Automat Wuhan 430072 Hubei Peoples R China

To achieve load disaggregation in non-intrusive load monitoring (NILM) system, a load event matching method based on graph theory is proposed, which is built on the improved Kuhn-Munkras algorithm. In this method, firstly, an adaptive fitting method using time window is applied to detect the load whether it is switched on and/or off. Particularly, to avoid the fluctuation of load signatures, the kernel density estimation is then built by a number of the independent features of the load switching on, including the active and reactive power signatures. The distribution of load signatures is thereby obtained, allowing the load event to be classified by its features. The load matching, which is based on the improved km algorithm, is then utilized to resolve the matrix formed by the matching probability of the load event. Similarly, load identification can also be realized by matching the features of events with the signatures in the database. Finally, the experimental results using datasets of our lab and REDD demonstrate that the proposed method can obtain the desirable result for load event matching, and promote the performance in load identification.

关键词： Switches Home appliances Event detection Load modeling Kernel Reactive power Databases Non-intrusive load event load matching km algorithm load identification

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An Improved km algorithm for Computing Structural Index of DAE System

An Improved KM Algorithm for Computing Structural Index of D...

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第12届分布式计算及其应用国际学术研讨会

作者： Yan Zeng Xuesong Wu Jianwen Cao Laboratory of Parallel Software and Computational Science of Software Institute of Software Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences

This paper proposes an improved km algorithm to computing the structural index of linear time-invariant Differential Algebraic Equation(DAE) systems. The problem is of practical significance in index reduction based on structural index of DAE system and combinatorial relaxation theory. This improved km algorithm combines greedy idea and classical km algorithm. It first computes matches as much as possible using greedy technology, and then call km algorithm to search the matches for the unmatched vertices during the step of greedy technology. The improved km algorithm reduces the running time bound by a factor of, the number of matches searched using greedy algorithm. Generally, the time complexity is, the optimal time is.

关键词： DAE Index Reduction Structural Index Bipartite Graph Greedy km algorithm

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Application of QPSO-km algorithm in Wine Quality Classification

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Agricultural Science & Technology 2015年第9期16卷 2045-2047页

作者：邱靖彭莞云吴瑞武张海涛云南农业大学教务处云南昆明650201 云南农业大学植物保护学院云南昆明650201 云南农业大学基础与信息工程学院云南昆明650201

Since there are many factors affecting the quality of wine, total 17 factors were screened out using principle component analysis. The difference test was conducted on the evaluation data of the two groups of testers. The results showed that the evaluation data of the second group were more reliable compared with those of the first group. At the same time, the km algorithm was optimized using the QPSO algorithm. The wine classification model was established. Compared with the other two algorithms, the QPSO-km algorithm was more capable of searching the globally optimum solution, and it could be used to classify the wine samples. In addition,the QPSO-km algorithm could also be used to solve the issues about clustering.

关键词： QPSO km algorithm Wine sample Classification model

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Dynamic tasks scheduling based on weighted bi-graph in Mobile Cloud Computing

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2018年 19卷 214-222页

作者： Wang, Tongxiang Wei, Xianglin Liang, Tao Fan, Jianhua Army Engn Univ PLA Nanjing 210007 Jiangsu Peoples R China Nanjing Telecommun Technol Res Inst Nanjing 210007 Jiangsu Peoples R China

The development of mobile computing and rapid growth of mobile applications have promoted the emergence of several edge-based computing paradigms, i.e. Fog computing, edge Computing, which can serve as the middle layer of end-user devices and the powerful cloud. As an implementation subtype of Fog computing, Mobile Cloud Computing (MCC) aims at leveraging limited resources available at network edge in order to enrich mobile applications and promote end-users' experience. Thus, an efficient resource allocation or scheduling scheme is vital for ensuring the effectiveness of MCC. In order to improve the performance of scheduling algorithm and promote its applications in practice, this paper proposes the Dynamic Tasks Scheduling algorithm based on Weighted Bi-graph model (DTSWB), which takes the dynamics of both tasks and providers into consideration. Specially, the scheduling problem is translated to be a maximum weighted bi-graph matching problem and an integer programming model is formulated. Then, the matching problem is solved by DTSWB, which mainly consists of four steps: state information collection of offloaded tasks and service providers, mapping relationship establishment, profit matrix determination and optimal matching based on Kuhn Munkras (km). At last, the effectiveness and validity of the proposed algorithm are verified by a series of simulations and the simulation results show that DTSWB achieves better performance than existing scheduling algorithms.

关键词： Mobile Cloud Computing Task scheduling Weighted bi-graph km algorithm

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A collaboration approach for cloud factories based on Kuhn-Munkras algorithm

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2022年第9期35卷 972-988页

作者： Huo, Yunliang Xiong, Ji Guo, Zhixing Wenjun, Xiao Lei, Liang Sichuan Univ Sch Mech Engn Chengdu Peoples R China Sichuan Univ Yibin R&d Pk Yibin Peoples R China Chengdu Yigao Intelligent Technol Co Ltd Chengdu Peoples R China

Cloud manufacturing (CMfg) is a network-based and service-oriented resource-sharing manufacturing paradigm, where resources from different factories are encapsulated as services that can be jointly employed to complete manufacturing tasks. To provide an efficient collaboration strategy among the factories in CMfg, in this work, a universal framework was proposed to describe manufacturing resources under the cloud environment, in which both equipment and factory levels were considered. Moreover, a collaboration model among cloud factories was presented from the perspective of the bipartite graph. Specifically, the surplus and demands of resources were mapped to vertex vectors of the bipartite graph, respectively. Collaboration expectations of each factory, QoS (quality of service) and supply-demand balance of manufacturing capacity were evaluated as the matching degree. Furthermore, the Kuhn-Munkras(km) algorithm, which is good at bipartite graph matching, was adopted to solve the proposed model. Finally, results from simulation confirmed that the proposed method is feasible and advanced, in comparison with the current related methods.

关键词： Cloud manufacturing Cloud factory Collaboration Strategy Optimization km algorithm

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On using extended statistical queries to avoid membership queries

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JOURNAL OF MACHINE LEARNING RESEARCH 2002年第3期2卷 359-395页

作者： Bshouty, NH Feldman, V Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

The Kushilevitz-Mansour (km) algorithm is an algorithm that finds all the "large" Fourier coefficients of a Boolean function. It is the main tool for learning decision trees and DNF expressions in the PAC model with respect to the uniform distribution. The algorithm requires access to the membership query (MQ) oracle. The access is often unavailable in learning applications and thus the km algorithm cannot be used. We significantly weaken this requirement by producing an analogue of the km algorithm that uses extended statistical queries (SQ) (SQs in which the expectation is taken with respect to a distribution given by a learning algorithm). We restrict a set of distributions that a learning algorithm may use for its statistical queries to be a set of product distributions with each bit being 1 with probability rho, 1/2 or 1 - rho for a constant 1/2 > rho > 0 (we denote the resulting model by SQ-D-rho). Our analogue finds all the "large" Fourier coefficients of degree lower than clog n (we call it the Bounded Sieve (BS)). We use BS to learn decision trees and by adapting Freund's boosting technique we give an algorithm that learns DNF in SQ-D-rho. An important property of the model is that its algorithms can be simulated by MQs with persistent noise. With some modifications BS can also be simulated by MQs with product attribute noise (i.e., for a query x oracle changes every bit of x with some constant probability and calculates the value of the target function at the resulting point) and classification noise. This implies learnability of decision trees and weak learnability of DNF with this non-trivial noise. In the second part of this paper we develop a characterization for learnability with these extended statistical queries. We show that our characterization when applied to SQ-Dp is tight in terms of learning parity functions. We extend the result given by Blum et al. by proving that there is a class learnable in the PAC model with random classification noise a

关键词： .denote boosting expectation decision trees learning algorithm uniform distribution PAC model Boolean function DNF km algorithm large Fourier

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STUDIES ON CENTROID TYPE-REDUCTION algorithmS FOR GENERAL TYPE-2 FUZZY LOGIC SYSTEMS

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2015年第6期11卷 1987-2000页

作者： Chen, Yang Wang, Dazhi Ning, Wu Northeastern Univ Inst Power Syst & Power Drives 3-11 Wenhua Rd Shenyang 110819 Liaoning Peoples R China Liaoning Univ Technol Coll Sci Guta Dist 121001 Jinzhou Peoples R China Liaoning Univ Technol Coll Elect & Informat Engn Guta Dist 121001 Jinzhou Peoples R China

Generally speaking, Karnik-Mendel algorithm is a standard way to calculate the centroid and perform type-reduction (TR) for interval type-2 fuzzy sets and systems. In this paper, an efficient centroid type-reduction strategy for general type-2 fuzzy sets is introduced based on Karnik-Mendel (km) algorithm, enhanced Karnik-Mendel (Ekm) algorithm, enhanced iterative algorithm+stopping condition (EIASC). The strategy uses the result of alpha-plane representation, performs the centroid type-reduction on each alpha-plane, and expands type-reduction algorithms for general type-2 fuzzy logic systems. Simulations performed and compared by each of three types of algorithms show that they usually need only several resolution of alpha values such that the defuzzified values converge to real values. Compared with the exhaustive computation method, the method can tremendously decrease the computation complexity from exponential into linear. So it provides the potential application value for general type-2 fuzzy logic systems.

关键词： General type-2 fuzzy logic systems alpha-plane km algorithm Ekm algorithm EIASC

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