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Monotonic classification is a common task in the field of multicriteria decision-making, in which features and decision obey a monotonic constraint. The dominance-based rough set theory is an important mathematical tool for knowledge acquisition in monotonic classification tasks (MCTs). However, existing dominance-based rough set models are very sensitive to noise information, and only a misclassified sample will lead to large errors in acquiring knowledge. This unstable phenomenon does not meet the requirements of practical applications. On the other hand, feature selection is supposedly an effective dimensionality reduction approach for classification tasks. In the real world, feature combinations with multiple correlations can often provide important classification information, where the multiple correlations include redundancy, complementarity, and interaction between features. To the best of our knowledge, most of the existing feature selection methods for MCTs only consider the relevance between features and decision, while ignoring the multiple correlations. To overcome these two drawbacks, in this article, we propose a robust fuzzy dominance rough set model, and develop a feature selection method that considers multiple correlations based on the robust model for MCTs. First, a soft fuzzy dominance rough set (SFDRS) with robustness is proposed. Second, a feature evaluation index considering multiple correlations is presented. Finally, a feature selection algorithm based on SFDRS is designed to select an optimal feature subset. Extensive experiments are conducted on 12 public datasets, and the results show that the SFDRS model has good robustness and the proposed feature selection algorithm has excellent classification performance. [ABSTRACT FROM AUTHOR]

To successfully implement the platoon control of connected and automated vehicles (CAVs), the model uncertainties, external disturbances, and time delays must be addressed. In this study, we propose a distributed Model Predictive Control scheme to achieve offset-free tracking for CAVs with model uncertainties and mixed disturbances which contains both stochastic and deterministic noises. A simple Vehicle-to-Vehicle communication flow with the short-distance and low-volume information exchange is adopted to reduce the effect of the communication delay and dropout. First, the deterministic and stochastic perturbances are addressed separately in constraints handling with an integrated control law based on the mismatched prediction model. Additional constraints on coupling input and output are imposed to guarantee the assumption about forecast trajectory. Then an observer gives an unbiased estimate of the state and integrated disturbance. Based on the estimation, the target calculator eliminates the steady tracking offset by designing a new target in real-time and the Min-Max Model Predictive Control steers the uncertain systems to the new target under the worst case within the domain of disturbances. Lastly, the Optimal Control Problem is reformulated and can be efficiently solved by existing solvers. Simulation results indicate that the proposed method shows superiority in interference rejection and offset-free tracking. [ABSTRACT FROM AUTHOR]

Attribute reduction is one of the most important preprocessing steps in machine learning and data mining. As a key step of attribute reduction, attribute evaluation directly affects classification performance, search time, and stopping criterion. The existing evaluation functions are greatly dependent on the relationship between objects, which makes its computational time and space more costly. To solve this problem, we propose a novel separability-based evaluation function and reduction method by using the relationship between objects and decision categories directly. The degree of aggregation (DA) of intraclass objects and the degree of dispersion (DD) of between-class objects are first defined to measure the significance of an attribute subset. Then, the separability of attribute subsets is defined by DA and DD in fuzzy decision systems, and we design a sequentially forward selection based on the separability (SFSS) algorithm to select attributes. Furthermore, a postpruning strategy is introduced to prevent overfitting and determine a termination parameter. Finally, the SFSS algorithm is compared with some typical reduction algorithms using some public datasets from UCI and ELVIRA Biomedical repositories. The interpretability of SFSS is directly presented by the performance on MNIST handwritten digits. The experimental comparisons show that SFSS is fast and robust, which has higher classification accuracy and compression ratio, with extremely low computational time. [ABSTRACT FROM AUTHOR]

Incremental feature selection approaches can improve the efficiency of feature selection used for dynamic datasets, which has attracted increasing research attention. Nevertheless, there is currently no work on incremental feature selection approaches for dynamic ordered data. Moreover, the monotonic classification effect of ordered data is easily affected by noise, so a robust feature evaluation metric is needed for feature selection algorithm. Motivated by these two issues, we investigate incremental feature selection approaches using a new conditional entropy with robustness for dynamic ordered data in this study. First, we propose a new rough set model, i.e., fuzzy dominance neighborhood rough sets (FDNRS). Second, a conditional entropy with robustness is defined based on FDNRS model, which is used as evaluation metric for features and combined with a heuristic feature selection algorithm. Finally, two incremental feature selection algorithms are designed on the basis of the above researches. Experiments are performed on ten public datasets to evaluate the robustness of the proposed metric and the performance of the incremental algorithms. Experimental results verify that the proposed metric is robust and our incremental algorithms are effective and efficient for updating reducts in dynamic ordered data. [ABSTRACT FROM AUTHOR]

Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user’s movement, we design a deep neural network (DNN) to predict CCM from user location and user speed, and the corresponding estimation method is named as ULCCME. A location denoising method is further developed to reduce the positioning error and improve the robustness of ULCCME. For cases when user location information is not available, we propose an interesting way that uses the environmental 3D images to predict the CCM, and the corresponding estimation method is named as SICCME. Simulation results show that both the proposed methods are effective and will benefit the subsequent channel estimation. [ABSTRACT FROM AUTHOR]

Insulated gate bipolar transistor (IGBT) has been widely used in the voltage-source-converter-based high-voltage direct current (VSC-HVDC). However, the electromagnetic interference (EMI) generated by its fast switching process affects the normal operation of the surrounding power equipment. The switching voltage and current of IGBT are the main sources of EMI. Different driving circuit parameters result in different IGBT switching characteristics. In this article, the switching model of IGBT is introduced first and the influence of driving parameters on EMI sources is analyzed. Based on the conducted EMI experiment platform of an IGBT module, the time-domain waveforms of switching voltage and current under different driving parameters are obtained and compared with the model. Then the interference voltage spectrums are measured by an EMI receiver under different driving parameters. The influence of driving parameters on conducted EMI in 0.15–30 MHz is then analyzed and explained by combining the result of time domain and frequency domain. This article comprehensively analyzes the relationship between driving parameters and EMI sources and summarizes the influence characteristics of driving parameters on common-mode (CM) and differential-mode (DM) noise. The conclusions can help the parameters design of driving board, which can effectively reduce the conducted EMI of the converter. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive control approach with fuzzy logic parameter tuning (AFLPT) for the energy management of electric vehicles that are using fuel cell battery hybrid systems. The controller is adaptive to different driving conditions including normal, regenerative, and overload conditions. Specifically, the power flow between the fuel cell (FC) and the Li-ion battery is controlled in real time to maintain the battery state of charge (SOC) at a desirable level while satisfying the FC dynamic constraints. For guaranteeing performance in different driving conditions, the FLPT is integrated with the adaptive controller. Moreover, theoretical properties of the designed controller are analyzed. Simulation and experiment results illustrate the effectiveness of the proposed strategy for FC-battery hybrid systems in electric vehicles. [ABSTRACT FROM PUBLISHER]

With thyristor-based full-bridge module (TFBM) embedded in the converter valve, an evolutional line-commutated converter (ELCC) is proposed to enhance the commutation failure (CF) immunity. The operating principle of the TFBM and the coordinated control strategy between the TFBM and series-connected valve are presented. The voltage–current stress of the TFBM is analyzed, and the method to select the capacitor parameter is also presented. Then, the ELCC high-voltage direct current (HVDC) under the presented control strategy is developed in PSCAD/EMTDC, and its dynamic responses under ac fault condition are compared with that of capacitor-commutated converter-based HVDC (CCC-HVDC) and conventional line-commutated converter (LCC) HVDC. A dual-infeed HVDC system with TFBM embedded in one HVDC link is also developed to further investigate the merits of TFBM. Finally, the increased capital cost and power loss of the ELCC-HVDC are discussed. The results show that the presented ELCC-HVDC has the ability to reduce the CF risk effectively and also exhibits favorable steady-state and dynamic performances. [ABSTRACT FROM PUBLISHER]