To address the problem of DC bus voltage surge caused by load demand fluctuation in an off-grid microgrid, here, adaptive energy optimization method based on a hybrid energy-storage system to maintain stability is presented. The consists three parts: average filtering algorithm, extracting fluctuating power load; supercapacitor terminal control, keeping near reference; and battery pack balance adjusting charge/discharge state charge for packs. In this proposed method, after low-pass filter...

Aggregating loads and resources on both the supply demand side of a virtual power plant (VPP) can enhance coordination between distributed generation systems grid, ultimately improving utilization rate economic benefits renewable energy. The energy storage system (ESS) has added benefit flexible demand-side resources, which effectively suppress output uncertainty units balance load fluctuations. This paper proposes an improved light robust (ILR) optimization method for ESS’s resource,...

Naive Bayes (NB) is an extremely simple and remarkably effective approach to classification learning, but its conditional independence assumption rarely holds true in real-world applications. Attribute weighting known as a flexible model via assigning each attribute different weight discriminatively improve NB. approaches can fall into two broad categories: filters wrappers. Wrappers receive bigger boost terms of accuracy compared with filters, the time complexity wrappers much higher than...

Rationale Mass spectrometry imaging (MSI) has been widely used in biomedical research fields. Each pixel MSI consists of a mass spectrum that reflects the molecule feature tissue spot. Because contains high‐dimensional datasets, it is highly desired to develop computational methods for data mining and constructing segmentation maps. Methods To visualize different regions based on features improve efficiency processing enormous data, we proposed strategy four procedures including...

Due to its simplicity, efficiency, and effectiveness, multinomial naive Bayes (MNB) has been widely used for text classification. As in (NB), assumption of the conditional independence features is often violated and, therefore, reduces classification performance. Of numerous approaches alleviating features, structure extension attracted less attention from researchers. To best our knowledge, only structure-extended MNB (SEMNB) proposed so far. SEMNB averages all weighted super-parent...

Naive Bayes (NB) is easy to construct but surprisingly effective, and it one of the top ten classification algorithms in data mining. The conditional independence assumption NB ignores dependency between attributes, so its probability estimates are often suboptimal. Hidden naive (HNB) adds a hidden parent each attribute, which can reflect dependencies from all other attributes. Compared with Bayesian network algorithms, offers significant improvements performance avoids structure learning....

Background: Due to the limited amount of mRNA in single-cell, there are always many missing values scRNA-seq data, making it impossible accurately quantify expression singlecell RNA. The dropout phenomenon makes detect truly expressed genes some cells, which greatly affects downstream analysis such as cell cluster and development trajectories. Objective: This research proposes an accurate deep learning method impute data. DSAE-Impute employs stacked autoencoders capture gene characteristics...

Link prediction is an important problem in network data mining, which dedicated to predicting the potential relationship between nodes network. Normally, link based on supervised classification will be trained a dataset consisting of set positive samples and negative samples. However, well-labeled training datasets with annotations are always inadequate real-world scenarios, contain large number unlabeled that may hinder performance model. To address this problem, we propose...

Classification is the main research target of many algorithms in data mining. Of all algorithms, decision trees are more preferred by researchers due to their clarity and readability. ID3, as a heuristic algorithm, fairly classic popular induction trees. The key ID3 choose information gain standard for testing attributes. however, tends attribute with values splitting node, this often not best attribute. In paper, improved based on dependency degree condition attributes used selecting...

The new isometric mapping dimensionality reduction algorithm with Incremental Generalized Regression Network has been primarily recognized for stripe surface defects images the typical characteristics of complex texture, non-uniform image size, asymmetrical number sample classes, variation illumination environment. This method is suitable to resolve problem “short circuit”, stored internal structure in lower dimension space. In addition, parameters influence on defect greatly reduced....

This paper presents an operation strategy of virtual power plant (VPP) in consideration uncertainty demand based on improved light robust (ILR) optimization. The proposed is the energy storage system to supply for rolling load demand. controls charge/discharge battery depend electricity charge cost, minimize cost customers during fluctuation, A simulation experiment implemented, and some results are presented validate effectiveness VPP.

View retraction statement:Statement of Retraction: Training an innovative physics-learned deep neural network by the Adam optimization method to simulate responses hybrid multilayer system