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 deplo...
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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.
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 computin...
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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.
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 ...
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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)).
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, fir...
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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.
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 o...
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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.
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....
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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.
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 complet...
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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.
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 laye...
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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.
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 mo...
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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
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 s...
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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.
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