Abstract-Voluntarily modulating neural activity plays a key role in brain-computer interface (BCI). In general, the self-regulated neural activation patterns are used in the current BCI systems involving the repetitive trainings with feedback for an attempt to achieve a high-quality control performance. With the limitation posed by the training procedure in most BCI studies, the present work aims to investigate whether directly modulating the neural activity by using an external method could facilitate the BCI control. We designed an experimental paradigm that combines anodal transcranial direct current stimulation (tDCS) with a motor imagery (MI)-based feedback EEG BCI system. Thirty-two young and healthy human subjects were randomly assigned to the real and sham stimulation groups to evaluate the effect of tDCS-induced EEG pattern changes on BCI classification accuracy. Results showed that the anodal tDCS obviously induces sensorimotor rhythm (SMR)-related event-related desynchronization (ERD) pattern changes in the upper-mu (10-14 Hz) and beta (14-26 Hz) rhythm components. Both the online and offline BCI classification results demonstrate that the enhancing ERD patterns could conditionally improve BCI performance. This pilot study suggests that the tDCS is a promising method to help the users to develop reliable BCI control strategy in a relatively short time.Index Terms-Brain-computer interface (BCI), motor imagery, neuro-modulation, transcranial direct current stimulation (tDCS).

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Daily water level forecasting is of significant importance for the comprehensive utilization of water resources. An improved least squares support vector machine (LSSVM) model was introduced by including an extra bias error control term in the objective function. The tuning parameters were determined by the cross-validation scheme. Both conventional and improved LSSVM models were applied in the short term forecasting of the water level in the middle reaches of the Yangtze River, China. Evaluations were made with both models through metrics such as RMSE (Root Mean Squared Error), MAPE (Mean Absolute Percent Error) and index of agreement (d). More accurate forecasts were obtained although the improvement is regarded as moderate. Results indicate the capability and flexibility of LSSVM-type models in resolving time sequence problems. The improved LSSVM model is expected to provide useful water level information for the managements of hydroelectric resources in Rivers. Energies 2019, 12, 112 2 of 11 artificial neural network (ANN) methods. Palani et al. [8] applied an ANN model for water quality estimation. Nourani et al. [9] established an ANN model for groundwater level prediction. Ivan and Gilja [10] showed good performance of ANNs for hydraulic parameter prediction. However, the model accuracy differs with neuron structures and parameter calibrations might be time-consuming.The support vector machine (SVM) has been used to address short-term forecasting problems since the 90s of the 20th century, on the basis of which LSSVM (least squares support vector machine) is put forward to overcome drawbacks (e.g., computation cost [11], uncertainties in structural parameter determination [12]) of SVM. LSSVM models solve a linear matrix equation with fewer constraint conditions and have been utilized in a variety of applications, e.g., forecasting of groundwater level fluctuations [13], river stage [14], and watershed runoff [15]. In the case of monthly flow forecasting, Noori et al. [16] discussed the influence of parameter selections on the model performance. Hybrid models have also been proved to be effective ways, such as SVM-Wavelet transform [17].Although the LSSVM models provide favorable solutions in hydrological forecasting problems, issues such as the kernel function and unbalanced features need to be carefully explored. Cheng et al. [18] improved LSSVM by integrating an adaptive time function. Thereby, the dynamic nature of the time series is considered by assigning an appropriate weight in the cash flow prediction for construction projects. To cope with low efficiency, Cong et al. [19] incorporated the fruit fly optimization algorithm (FOA) for appropriate parameter values of LSSVM. Comparison between LS-SVM-FOA and other models indicated the superiority of the improved model. Ghorbani et al.[20] modelled river discharge time series using SVM and ANN. The authors conclude that SVM and ANN have an edge over the results by the conventional RC (Rating Curve) and MLR (Multiple Linear Regression) models. This is mor...

Recent research has demonstrated the effectiveness of deep subspace learning networks, including the principal component analysis network (PCANet) and linear discriminant analysis network (LDANet), since they can extract high-level features and better represent abstract semantics of given data. However, their representation does not consider the nonlinear relationship of data and limits the use of features with nonlinear metrics. In this paper, we propose a novel architecture combining the kernel collaboration representation with deep subspace learning based on the PCANet and LDANet for facial expression recognition. First, the PCANet and LDANet are employed to learn abstract features. These features are then mapped to the kernel space to effectively capture their nonlinear similarities. Finally, we develop a simple yet effective classification method with squared [Formula: see text]-regularization, which improves the recognition accuracy and reduces time complexity. Comprehensive experimental results based on the JAFFE, CK[Formula: see text], KDEF and CMU Multi-PIE datasets confirm that our proposed approach has superior performance not just in terms of accuracy, but it is also robust against block occlusion and varying parameter configurations.

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Automatic Identification System (AIS), as a major data source of navigational data, is widely used in the application of connected ships for the purpose of implementing maritime situation awareness and evaluating maritime transportation. Efficiently extracting featured data from AIS database is always a challenge and time-consuming work for maritime administrators and researchers. In this paper, a novel approach was proposed to extract massive featured data from the AIS database. An Evidential Reasoning rule based methodology was proposed to simulate the procedure of extracting routes from AIS database artificially. First, the frequency distributions of ship dynamic attributes, such as the mean and variance of Speed over Ground, Course over Ground, are obtained, respectively, according to the verified AIS data samples. Subsequently, the correlations between the attributes and belief degrees of the categories are established based on likelihood modeling. In this case, the attributes were characterized into several pieces of evidence, and the evidence can be combined with the Evidential Reasoning rule. In addition, the weight coefficients were trained in a nonlinear optimization model to extract the AIS data more accurately. A real life case study was conducted at an intersection waterway, Yangtze River, Wuhan, China. The results show that the proposed methodology is able to extract data very precisely.

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