In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis metho...
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ISBN:
(纸本)9781509006243
In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis method, usually sample the reference points via a set of uniformly distributed weight vectors generated on an ideal hyper-plane in objective space, which however often ignore the geometric shape of a specific Pareto front. Therefore, we propose a novel reference point sampling approach by taking the specific shape of the Pareto optimal front to be tackled into account for measuring the performance of multi-objective evolutionary algorithms. The performance of the proposed reference point sampling method against the other two state-of-the-art sampling methods is tested on six test instances in various conditions, which clearly demonstrate the effectiveness and superiority of the proposed sampling method.
In this paper, we formulate the digital inpainting as a curvature-driven anisotropic concentration difiusing process. The difiusion strength along the gradient directions is defined as a function of curvature, so that...
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Empirical mode decomposition (EMD) provides a powerful tool for adaptive multiscale analysis of nonstationary signals. Bidimensional empirical mode decomposition (BEMD) techniques decompose an image into several bidim...
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ISBN:
(纸本)9781467391672
Empirical mode decomposition (EMD) provides a powerful tool for adaptive multiscale analysis of nonstationary signals. Bidimensional empirical mode decomposition (BEMD) techniques decompose an image into several bidimensional intrinsic mode functions (BIMFs) and a bidimensional residue (BR). Firstly, several polarization images can be decomposed into several BIMFs with multi-scales using BEMD. For the BIMF coefficients, the teager energy-based method is used. For the each BIMF coefficients, the area-based teager energy larger value of information measurement is used to select the better coefficients for fusion. At last the fused image can be obtained by utilizing inverse transform for fused image. Experimental results show that the proposed algorithm gives more satisfactory results than the traditional image fusion algorithms in preserving the edges and texture information.
IEEE 802.11 is a protocol standard widely used in wireless local area network (WLAN). For the distributed coordination function (DCF) of IEEE 802.11 MAC layer, the binary exponential backoff (BEB) algorithm has the de...
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IEEE 802.11 is a protocol standard widely used in wireless local area network (WLAN). For the distributed coordination function (DCF) of IEEE 802.11 MAC layer, the binary exponential backoff (BEB) algorithm has the defect of poor access fairness. To address this issue, the design criteria of backoff algorithm is generalized from the analysis of the key performance parameters of DCF, and then a logarithmic backoff (LB) algorithm based on dynamic contention window (CW) adjustment is proposed. The algorithm dynamically adjusts the initial value of CW and the backoff size of the CW by using the logarithmic function that takes the number of network competing nodes as the variable. Simulation results show that compared with the BEB algorithm, the LB algorithm can effectively improve the fairness performance and the throughput performance, and also reduce network delay.
Randomized cyclic delay diversity (RCDD) is an effective means to capture both the space diversity and the frequency diversity with low complexity detection over frequency-selective fading channels. Since many existin...
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The concept of deep learning has been applied to many domains, but the definition of a suitable problem depth has not been sufficiently explored. In this study, we propose a new Hierarchical Covering Algorithm (HCA)...
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The concept of deep learning has been applied to many domains, but the definition of a suitable problem depth has not been sufficiently explored. In this study, we propose a new Hierarchical Covering Algorithm (HCA) method to determine the levels of a hierarchical structure based on the Covering Algorithm (CA). The CA constructs neural networks based on samples' own characteristics, and can effectively handle multi-category classification and large-scale data. Further, we abstract characters based on the CA to automatically embody the feature of a deep structure. We apply CA to construct hidden nodes at the lower level, and define a fuzzy equivalence relation R on upper spaces to form a hierarchical architecture based on fuzzy quotient space theory. The covering tree naturally becomes from R. HCA experiments performed on MNIST dataset show that the covering tree embodies the deep architecture of the problem, and the effects of a deep structure are shown to be better than having a single level.
Accurate endpoint detection is crucial for speech recognition accuracy. This paper presents a new technique for speech endpoint detection in a noisy environment based on the empirical mode decomposition(EMD) algorithm...
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Accurate endpoint detection is crucial for speech recognition accuracy. This paper presents a new technique for speech endpoint detection in a noisy environment based on the empirical mode decomposition(EMD) algorithm and higher order statistics. With the EMD, the noise speech signals can be decomposed into a sum of the band-limited function called intrinsic mode functions(IMFs), which is a zero-mean AM-FM component. Then higher order statistics of the IMF components can be used to extract the desired feature for endpoint detection. In order to show the effectiveness of the proposed method, we present examples showing that the new measure is more effective than traditional measures. The experimental results show that the performance of the proposed algorithm is noticeable in the real speech signal tests with different SNR.
Since the single-antenna relay cooperative communication systems have a serious defect in reliability of data transmission, while the MIMO communication systems have difficulty in design and decoding, we propose a mix...
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Since the single-antenna relay cooperative communication systems have a serious defect in reliability of data transmission, while the MIMO communication systems have difficulty in design and decoding, we propose a mixed structure of full-rate and low-detection complexity cooperative relay transmission scheme based on full-rate relay cooperative transmission model, which combines the advantages of cyclic delay diversity (CDD) technology and linear constellation precoding (LCP) technology. This scheme can excavation diversity gain by using single antenna terminal and multiple antenna simultaneously, moreover, the decoding complexity is low. The bit error rate (BER) performance is improved with the number of the antenna or the multipath or the relay nodes increases.
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