The paper is concerned with the problems of rough sets theory and rough classification of objects. It is a new approach to problems from the field of decision-making, data analysis, knowledge representation, expert sy...
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The paper is concerned with the problems of rough sets theory and rough classification of objects. It is a new approach to problems from the field of decision-making, data analysis, knowledge representation, expert systems etc. Several applications (particularly in medical diagnosis and engineeringcontrol) confirm the usefulness of the rough sets idea. Rough classification concerns objects described by multiple attributes in a so-called information system. Traditionally, the information system is assumed to be complete, i.e. the descriptors are not missing and are supposed to be precise. In this paper we investigate the case of incomplete information systems, and present a generalization of the rough sets approach which deals with missing and imprecise descriptors.
Clear imagery of retinal vessels is one of the critical shreds of evidence in specific disease diagnosis and evaluation, including sophisticated hierarchical topology and plentiful-and-intensive capillaries. In this w...
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There have been numerous methods for learning and predicting time series ranging from the traditional time-series analyses to recent approaches using neural networks. A central issue common to all of them is the deter...
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There have been numerous methods for learning and predicting time series ranging from the traditional time-series analyses to recent approaches using neural networks. A central issue common to all of them is the determination of model structure. Both mean prediction error and An Information Criterion (AIC) are useful in model selection;the model with the smallest mean prediction error or AIC is selected from among a set of models as the best one. In this way they give a solution to the problem of model selection. Due to huge search space, however, the mean prediction error or AIC alone is not powerful enough to find the best model structure from among all the candidates. In the present paper the authors propose to use both a structural learning with forgetting and the mean prediction error or AIC to find a model with better generalization ability. Jordan networks and buffer networks, popular in the modeling of time series, are examined in this paper. The structural learning with forgetting and backpropagation (BP) learning are applied to compare the learning and prediction performance of these two types of models. Simulation results demonstrate that the structural learning with forgetting has better generalization ability than BP learning both in Jordan networks and buffer networks.
To solve the multi-user semantic communication network capacity bottlenecks, this paper proposes a semantic feature domain multiple access (SFDMA) scheme. By extracting and encoding semantic features and jointly proce...
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Dear Editor,This letter proposes a contrastive consensus graph learning model for multi-view *** are usually built to outline the correlation between multi-model objects in clustering task,and multiview graph clusteri...
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Dear Editor,This letter proposes a contrastive consensus graph learning model for multi-view *** are usually built to outline the correlation between multi-model objects in clustering task,and multiview graph clustering aims to learn a consensus graph that integrates the spatial property of each view.
This study investigated the oil pollution remediation ability of konjac glucomannan (KGM) aerogel immobilized Chlorella vulgaris LH-1 (C. vulgaris). The KGM aerogel exhibited a high porosity of 86.83 %, and with the c...
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A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm ad...
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A self-adaptive learning based immune algorithm （SALIA） is proposed to tackle diverse optimization problems, such as complex multi-modal and ill-conditioned prc,blems with the high robustness. The SALIA algorithm adopted a mutation strategy pool which consists of four effective mutation strategies to generate new antibodies. A self-adaptive learning framework is implemented to select the mutation strategies by learning from their previous performances in generating promising solutions. Twenty-six state-of-the-art optimization problems with different characteristics, such as uni-modality, multi-modality, rotation, ill-condition, mis-scale and noise, are used to verify the validity of SALIA. Experimental results show that the novel algorithm SALIA achieves a higher universality and robustness than clonal selection algorithms （CLONALG）, and the mean error index of each test function in SALIA decreases by a factor of at least 1.0×10^7 in average.
Most existing generation scheduling models for power systems under demand uncertainty rely on energy-based formulations with a finite number of time periods, which may fail to ensure that power supply and demand are b...
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In compressive sensing(CS) based inverse synthetic aperture radar(ISAR) imaging approaches, the quality of final image significantly depends on the number of measurements and the noise level. In this paper, we propose...
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In compressive sensing(CS) based inverse synthetic aperture radar(ISAR) imaging approaches, the quality of final image significantly depends on the number of measurements and the noise level. In this paper, we propose an improved version of CSbased method for inverse synthetic aperture radar(ISAR) imaging. Different from the traditional l1 norm based CS ISAR imaging method, our method explores the use of Gini index to measure the sparsity of ISAR images to improve the imaging quality. Instead of simultaneous perturbation stochastic approximation(SPSA), we use weighted l1 norm as the surrogate functional and successfully develop an iteratively re-weighted algorithm to reconstruct ISAR images from compressed echo samples. Experimental results show that our approach significantly reduces the number of measurements needed for exact reconstruction and effectively suppresses the noise. Both the peak sidelobe ratio(PSLR) and the reconstruction relative error(RE) indicate that the proposed method outperforms the l1 norm based method.
The paper deals with the new methodology of programming of sequential control systems in Lab VIEW language. By similarity to the SFC language and use of only a few defined blocks and connections, the proposed approach...
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