To develop an advanced CBR system to well adapt to the intelligence implementation of new engineering process in the big data environment, Bayesian network (BN) model is introduced to CBR system for knowledge reasonin...
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To develop an advanced CBR system to well adapt to the intelligence implementation of new engineering process in the big data environment, Bayesian network (BN) model is introduced to CBR system for knowledge reasoning. However, as engineering application is becoming more and more complicated, the number of parameters used to define engineering application grows larger and larger, leading to the seriously reduced efficiency as well as the accuracy of the integrated model. For the problem of reduced efficiency, this paper proposes In-External (ie) algorithm to perform the assignment of big data distribution for parallel data processing, which can fully utilize the capacity of Hadoop system and attain the best efficiency of knowledge reasoning. For the problem of reduced accuracy, in view of the fact that traditional probability learning methods are unfit for the proposed CBR system, this paper proposes Discount Exponential Coefficients of Multivariate Beta Distribution (DECMBD) algorithm to conduct the probability learning of proposed system. In DECMBD algorithm, a discount ratio is given to each exponential coefficient of multivariate Beta distribution to improve the occurrence times counting of all problem features and then gain better effect of probability learning. Finally, lots of experiments are performed to validate the effectiveness of the proposed advanced CBR system. (c) 2020 Elsevier Ltd. All rights reserved.
In this study, the problems, which simultaneously extract and direct positioning of multiple transmitters, are considered. Without assuming a priori knowledge of the source number, the multiple signal classification (...
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In this study, the problems, which simultaneously extract and direct positioning of multiple transmitters, are considered. Without assuming a priori knowledge of the source number, the multiple signal classification (MUSIC) algorithm is applied after using the Akaike information theoretic criteria for model order determination, while the minimum variance distortionless response (MVDR) algorithm can be directly used to localise multiple sources that transmit unknown signals. Two methods, which combine MUSIC and MVDR with the image expansion (ie) algorithm, are proposed. The ie algorithm used in actual scenarios is based on the constant-false-alarm rate. Simulation results show that the proposed algorithms can effectively extract and accurately localise multiple transmitters.
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